Sensing for heart failure management

ABSTRACT

In some examples, determining a heart failure status using a medical device comprising one or more sensors includes determining a first value of a heart beat variability metric of a patient while an activity state of a patient satisfies an inactivity criterion based on a signal received from the one or more sensors, and determining, within a predetermined period of time after further determining that the activity state of the patient no longer satisfies the inactivity criterion, a second value of the heart beat variability metric while the activity state of the patient no longer satisfies the inactivity criterion based on the signal. A difference between the first value of the heart beat variability metric and the second value of the heart beat variability metric may be determined and the heart failure status of the patient may be determined based on the difference.

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/842,714, filed May 3, 2019, the entire contentof which is incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates generally to medical device systems, and, moreparticularly, medical device systems configured to monitor patientparameters.

BACKGROUND

Some types of medical devices may be used to monitor one or morephysiological parameters of a patient, such as physiological parametersassociated with cardiac function. Such medical devices may include, ormay be part of a system that includes, sensors that detect signalsassociated with such physiological parameters; e.g., heart rateparameters. Values determined based on such signals may be used toassist in detecting changes in medical conditions, in evaluating theefficacy of a therapy, or in generally evaluating patient health.

Medical devices that monitor physiological parameters related to amedical condition of a patient may evaluate values associated with thephysiological parameters, such as to determine whether the values exceeda threshold or have changed over time. Values that exceed a threshold orthat have changed may indicate that a therapy being administered to thepatient is not effectively managing the patient's medical condition.

SUMMARY

In general, this disclosure is directed to techniques for determining aheart failure status of a patient, such as a patient diagnosed with aheart failure condition. Such techniques may include performingassessments associated with aspects of a patient's cardiac function,such as determining a heart beat variability (HBV) metric, anddetermining the heart failure status of the patient based on the outcomeof the assessments. Ongoing monitoring of aspects of the patient'scardiac function associated with a patient's condition (e.g., heartfailure condition) may enable detection of changes in cardiac functionbefore such changes lead to symptoms, acute decompensation,hospitalization and/or the progression or development of one or moremedical conditions.

Some example techniques may include determining, by a medical devicesystem including a medical device, a heart failure status of a patientbased on at least one HBV metric, such as based on a difference betweena first value of at least one HBV metric of the patient and a secondvalue of the at least one HBV metric of the patient, or a comparison ofa current value of at least one HBV metric of the patient to a baselinevalue of the at least one HBV metric. As an example, a first value of atleast one HBV metric may be determined, based on at least one firstsignal received from one or more sensors (e.g., electrodes) of themedical device, while an activity state of the patient satisfies atleast one inactivity criterion. The inactivity criterion may be a valueof at least one of a patient activity level, a patient posture, a timeof day, a patient heart rate, a patient respiration rate, or any othersuitable criterion associated with a substantially inactive state of thepatient. The second value of the at least one HBV metric may bedetermined, based on at least one second signal received from the one ormore sensors, after determining the first value of the at least one HBVmetric and while the activity state of the patient no longer satisfiesthe at least one inactivity criterion. Thus, the first value of the atleast one HBV metric may be determined when the patient is substantiallyinactive (e.g., in a sleep state) and the second value of the at leastone HBV metric may be determined when the patient is more active (e.g.,in a waking state) than when the first value of the at least one HBVmetric was determined. As discussed herein, determining a heart failurestate of the patient based on changes in a value of at least one HBVmetric associated with changes in an activity state of the patient mayenable determination of a possibility that the patient will experiencean adverse medical event and/or benefit from medical intervention.

The second value of the at least one HBV metric may be determined withina predetermined period of time after the activity state of the patientno longer satisfies the at least one inactivity criterion. For example,the second value of the at least one HBV metric may be determined withina predetermined period of time after patient activity has increasedrelative to a period of substantial patient inactivity (e.g., sleep)during which the first value of the at least one HBV metric wasdetermined. A change in the at least one HBV metric occurring during thepredetermined period of time, reflected by difference between the firstvalue of the at least one HBV metric and the second value of the atleast one HBV metric, may be indicative of the heart failure status ofthe patient.

Some other example techniques described herein may include determining aheart failure status of a patient based on a difference between acurrent value of at least one HBV metric and a baseline value of the atleast one HBV metric. Such techniques may be used, for example, tomonitor changes in an absolute value of the at least one HBV metricoccurring during a particular portion of an activity cycle of a patientover the course of multiple activity cycles. An activity cycle of thepatient may be a period of time in which the activity state of thepatient both satisfies the at least one inactivity criterionsubsequently no longer satisfies the at least one inactivity criterion,such as a period of one day. In some examples, monitoring changes in anabsolute value of the at least one HBV metric occurring during aparticular portion of an activity cycle of the over the course ofmultiple activity cycles may enable monitoring of different aspects of aheart failure status of the patient relative to monitoring changes in adifference between values of at least one HBV metric that occur duringdifferent portions of an activity cycle of the over the course ofmultiple activity cycles.

In any such examples, a value of a difference between first and secondvalues of at least one HBV metric or a value of a difference between acurrent value of at least one HBV metric and a baseline value of the atleast one HBV metric may be expected to be within a particular range ina patient who does not have a heart failure condition or who has a heartfailure condition that is adequately compensated for by therapy. Adetermined value such a difference that is not within the range may beindicative of a developing or worsening heart failure condition. Thus,such a technique may include determining whether such a differencesatisfies one or more HBV difference threshold values and/or one or moreHBV threshold values. Determining the heart failure status of thepatient further may include determining a possibility that the patientwill experience an adverse medical event (e.g., worsening heart failurefor which medical intervention may be beneficial) and/or transmittingthe heart failure status of the patient to a remote computer for reviewby a clinician or other user.

In some other techniques, a clinician may determine the patient's heartfailure status based on results of diagnostic or other evaluativeprocedures carried out during a clinician visit and prescribe treatmentaccordingly. For example, the clinician may prescribe medication and/ordetermine patient-specific values of one or more parameters at which amedical device may deliver electrical stimulation therapy (e.g.,anti-arrhythmia pacing, cardiac resynchronization therapy (CRT), and/orother types of electrical stimulation therapy) to the patient's heart.However, the patient's treatment needs may change between clinicianvisits as the patient's heart-failure condition progresses or otherwisechanges. Thus, ongoing monitoring of values of at least one HBV metricbetween clinician visits may enable early detection of changes incardiac function before the changes lead to an adverse medical eventsuch as recurrent symptoms, acute decompensation, and/or the progressionor the patient's heart failure condition or development of one or moreadditional medical conditions.

Consequently, clinical outcomes may benefit from methods for determininga heart failure status of a patient based on determined values of atleast one HBV metric of the patient between clinician visits, which inturn may enable early detection of heart failure progression and/orprediction of a possibility of hospitalization or other medical event.In response to such information, a patient's treatment may be adjusted(e.g., by modifying a drug regimen or values of one or more parametersat which a medical device may deliver electrical stimulation therapy).Prompt adjustment of one or more aspects of a patient's heart failuretreatment as the patient's heart failure condition changes may helpreduce the patient's possibility of acute decompensation,hospitalization, or development of additional medical conditions.

Accordingly, techniques described herein may enable periodicdetermination of a heart failure status of a patient between clinicianvisits. In some techniques described herein, a medical device systemthat includes a medical device may determine a patient's heart failurestatus and transmit the heart failure status to a remote computer orother device external to the patient. In some cases, the patient's heartfailure status may indicate the patient's possibility of acutedecompensation or hospitalization based on the heart failure. The remotecomputer then may transmit instructions for a medical intervention(e.g., instructions for changes to a drug regimen), to a user deviceused by the patient or a caregiver. In this manner, a patient'sdiagnoses and/or treatment for a heart failure condition may be modifiedas needed in between clinic visits, which may help avoid adverse medicalevents such as recurrent symptoms or hospitalization.

In one example, a method for determining a heart failure status of apatient using a medical device comprising one or more sensors comprises,by processing circuitry of a medical device system comprising themedical device, determining that an activity state of the patientsatisfies at least one inactivity criterion based on at least one firstsignal received from the one or more sensors; determining a first valueof at least one heart beat variability metric of the patient while theactivity state of the patient satisfies the at least one inactivitycriterion based on at least one second signal received from the one ormore sensors, determining, after determining the first value of the atleast one heart beat variability metric, that the activity state of thepatient no longer satisfies the at least one inactivity criterion basedon the at least one first signal, and determining, within apredetermined period of time after determining that the activity stateof the patient no longer satisfies the at least one inactivitycriterion, a second value of the at least one heart beat variabilitymetric while the activity state of the patient no longer satisfies theat least one inactivity criterion based on the at least one secondsignal. The method further comprises determining a difference betweenthe first value of the at least one heart beat variability metric andthe second value of the at least one heart beat variability metric, anddetermining the heart failure status of the patient based on thedifference.

In another example, a system for determining a heart failure status of apatient using a medical device comprises the medical device, wherein themedical device comprises one or more sensors, and processing circuitry.The processing circuitry is configured to determine that an activitystate of the patient satisfies at least one inactivity criterion basedon at least one first signal received from the one or more sensors,determine a first value of at least one heart beat variability metric ofthe patient while the activity state of the patient satisfies the atleast one inactivity criterion based on at least one second signalreceived from the one or more sensors, determine, after determining thefirst value of the at least one heart beat variability metric, that theactivity state of the patient no longer satisfies the at least oneinactivity criterion based on the at least one first signal, anddetermine, within a predetermined period of time after determining thatthe activity state of the patient no longer satisfies the at least oneinactivity criterion, a second value of the at least one heart beatvariability metric while the activity state of the patient no longersatisfies the at least one inactivity criterion based on the at leastone second signal. The processing circuitry is further configured todetermine a difference between the first value of the at least one heartbeat variability metric and the second value of the at least one heartbeat variability metric, and determine the heart failure status of thepatient based on the difference.

In another example, a non-transitory computer-readable medium storesinstructions for causing processing circuitry to perform a method fordetermining a heart failure status of a patient using a medical devicecomprising one or more sensors, the method comprising, by processingcircuitry of a medical device system comprising the medical devicedetermining that an activity state of the patient satisfies at least oneinactivity criterion based on at least one first signal received fromthe one or more sensors, determining a first value of at least one heartbeat variability metric of the patient while the activity state of thepatient satisfies the at least one inactivity criterion based on atleast one second signal received from the one or more sensors,determining, after determining the first value of the at least one heartbeat variability metric, that the activity state of the patient nolonger satisfies the at least one inactivity criterion based on the atleast one first signal, and determining, within a predetermined periodof time after determining that the activity state of the patient nolonger satisfies the at least one inactivity criterion, a second valueof the at least one heart beat variability metric while the activitystate of the patient no longer satisfies the at least one inactivitycriterion based on the at least one second signal. The method furthercomprises determining a difference between the first value of the atleast one heart beat variability metric and the second value of the atleast one heart beat variability metric, and determining the heartfailure status of the patient based on the difference.

In another example, a method for determining a heart failure status of apatient using a medical device comprising one or more sensors comprises,by processing circuitry of a medical device system comprising themedical device, determining that an activity state of the patientsatisfies at least one inactivity criterion based on at least one firstsignal received from the one or more sensors, determining that theactivity state of the patient has increased by determining that theactivity state of the patient no longer satisfies the at least oneinactivity criterion based on the at least one first signal afterdetermining that the activity state of the patient satisfies the atleast one inactivity criterion, and determining, within a predeterminedperiod of time after determining that the activity state of the patienthas increased, a current value of at least one heart beat variabilitymetric while the activity state of the patient no longer satisfies theat least one the inactivity criterion based on at least one secondsignal received from the one or more sensors. The method furthercomprises comparing the current value of the at least one heart beatvariability metric to a baseline value of the at least one heart beatvariability metric, and determining the heart failure status of thepatient based on the comparison.

In another example, a system for determining a heart failure status of apatient using a medical device comprises the medical device, wherein themedical device comprises one or more sensors, and processing circuitry.The processing circuitry is configured to determine that an activitystate of the patient satisfies at least one inactivity criterion basedon at least one first signal received from the one or more sensors,determine that the activity state of the patient has increased bydetermining that the activity state of the patient no longer satisfiesthe at least one inactivity criterion based on the at least one firstsignal after determining that the activity state of the patientsatisfies the at least one inactivity criterion, and determine, within apredetermined period of time after determining that the activity stateof the patient has increased, a current value of at least one heart beatvariability metric while the activity state of the patient no longersatisfies the at least one the inactivity criterion based on at leastone second signal received from the one or more sensors. The processingcircuitry is further configured to compare the current value of the atleast one heart beat variability metric to a baseline value of the atleast one heart beat variability metric, and determine the heart failurestatus of the patient based on the comparison.

In another example, a non-transitory computer-readable medium storinginstructions for causing processing circuitry to perform a method fordetermining a heart failure status of a patient using a medical devicecomprising one or more sensors, the method comprising, by processingcircuitry of a medical device system comprising the medical devicedetermining that an activity state of the patient satisfies at least oneinactivity criterion based on at least one first signal received fromthe one or more sensors, determining that the activity state of thepatient has increased by determining that the activity state of thepatient no longer satisfies the at least one inactivity criterion basedon the at least one first signal after determining that the activitystate of the patient satisfies the at least one inactivity criterion,and determining, within a predetermined period of time after determiningthat the activity state of the patient has increased, a current value ofat least one heart beat variability metric while the activity state ofthe patient no longer satisfies the at least one the inactivitycriterion based on at least one second signal received from the one ormore sensors. The method further comprises comparing the current valueof the at least one heart beat variability metric to a baseline value ofthe at least one heart beat variability metric, and determining theheart failure status of the patient based on the comparison.

This summary is intended to provide an overview of the subject matterdescribed in this disclosure. It is not intended to provide an exclusiveor exhaustive explanation of the apparatus and methods described indetail within the accompanying drawings and description below. Thedetails of one or more aspects of the disclosure are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages of this disclosure will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual drawing illustrating an example of a medicaldevice system including a leadless implantable medical device and anexternal device in conjunction with a patient;

FIG. 2 is a conceptual drawing illustrating an example configuration ofthe leadless implantable medical device of the medical device system ofFIG. 1 ;

FIG. 3 is a functional block diagram illustrating another perspective ofthe example configuration of the leadless implantable medical device ofFIG. 1 ;

FIGS. 4A and 4B are block diagrams illustrating other example leadlessimplantable medical devices substantially similar to the implantablemedical device of FIG. 1 ;

FIG. 5 is a block diagram illustrating an example system that includesan external device, such as a server, and one or more computing devicesthat are coupled to the leadless implantable medical device of FIG. 1and the external device of FIG. 1 via a network;

FIG. 6 is a flow diagram illustrating an example technique fordetermining a heart failure status of a patient based on a differencebetween a first value of at least one HBV metric of the patient and asecond value of the at least one HBV metric of the patient andtransmitting the heart failure status to a remote computer;

FIG. 7 is a flow diagram illustrating another example technique fordetermining a heart failure status of a patient based on a comparison ofa current value of at least one HBV metric of the patient to a baselinevalue of the at least one HBV metric of the patient, and transmittingthe heart failure status to a remote computer; and

FIG. 8 is a flow diagram illustrating an example technique for a remotecomputer to determine instructions for a medical intervention based on aheart failure status of a patient received from the leadless implantablemedical device of FIG. 1 and transmit the instructions to a userinterface.

DETAILED DESCRIPTION

In general, this disclosure describes example techniques and systemsrelated to determining a heart failure status of a patient based onvalues of one or more HBV metrics associated with cardiac function ofthe patient and determined during one or more particular activity statesof the patient. Processing circuitry of a medical device comprising oneor more sensors (e.g., one or more electrodes, accelerometers, or othersensors), or a system that includes the medical device, may determinethat an activity state of the patient satisfies at least one inactivitycriterion based on at least one first signal received from the one ormore sensors. For example, the processing circuitry may determine thatthe activity state of the patient satisfies the at least one inactivitycriterion by determining that a value of at least one of a patientactivity level, a patient posture, a time of day, a patient heart rate,or a patient respiration rate is indicative of patient inactivity and/orsleep. In examples in which the at least one inactivity criterionincludes a time of day, the processing circuitry may be configured toaccount for changes in time that may occur, such as when the patienttravels from one time zone to another time zone and/or whendaylight-savings time begins or ends. As described herein, changes invalues of one or more HBV metrics that occur with changes in an activitystate, such as an increase in an activity level of the patient, may beassociated with a heart failure status of the patient.

While the activity state of the patient satisfies the at least oneinactivity criterion, the processing circuitry may determine a firstvalue of at least one HBV metric of the patient based on at least onesecond signal received from the one or more sensors, which may comprisea plurality of electrodes. In such examples, the at least one secondsignal may be a cardiac electrogram signal received from at least two ofthe plurality of electrodes. In some examples, the at least oneinactivity criterion may be associated with a sleep state of thepatient. Thus, in some examples, the first value of the at least one HBVmetric that the processing circuitry determines during the predeterminedperiod of time may be a sleeping value of the at least one HBV metric.

The processing circuitry may determine, after determining the firstvalue of the at least one HBV metric, that the patient activity state nolonger satisfies the at least one patient inactivity criterion. A changein the patient activity state such that the patient activity state nolonger satisfies the at least one patient inactivity criterion may beassociated with the patient awakening from sleep or otherwise increasinghis or her activity level. Within a predetermined period of time afterdetermining that the activity state of the patient no longer satisfiesthe at least one inactivity criterion and while the activity state ofthe patient no longer satisfies the at least one inactivity criterion,the processing circuitry may determine a second value of the at leastone HBV metric based on the at least one second signal. In examples inwhich the at least one inactivity criterion is associated with a sleepstate of the patient, the processing circuitry may determine that thepatient activity state no longer satisfies the at least one patientinactivity criterion when the patient activity state has no longersatisfied the at least one patient inactivity criterion for a period oftime that satisfies an associated threshold value. In some suchexamples, the threshold value may be associated with the patient beinglikely to have awakened for the day or another relatively extendedduration, rather than only briefly awakening and then returning to asleep state. In some such examples, it may be desirable to excludeinstances in which the patient only briefly awakens fromcharacterization as the patient no longer being in a sleep state atleast because changes in a value of at least one HBV metric of interestmay not occur within the predetermined period of time after such briefwaking periods.

The predetermined period of time may be associated with a period oftime, after the activity state of the patient changes from satisfying atleast one inactivity criterion to no longer satisfying the at least oneinactivity criterion, during which changes in the value of the at leastone HBV metric associated with changes in activity state are expected tooccur. For example, the predetermined period of time may be a period oftime after the patient has awakened from a sleep state. Thus, in someexamples, the second value of the at least one HBV metric that theprocessing circuitry determines during the predetermined period of timemay be a waking value of the at least one HBV metric. In some examples,the predetermined period of time may be about 30 minutes, such as about15-30 minutes.

In some examples, the processing circuitry may determine, prior todetermining that the activity state of the patient no longer satisfiesthe at least one inactivity criterion, that a period of time duringwhich the activity state of the patient is expected to satisfy the atleast one inactivity criterion has elapsed. Such a period of time may beassociated with a period of time during which the patient is expected tobe asleep or during which the activity state of the patient otherwise isexpected to satisfy at least one inactivity criterion, and theexpiration of such a period of time may be associated with a time atwhich the patient is expected to awaken or during which the activitystate of the patient otherwise is expected to no longer satisfy the atleast one inactivity criterion. The period of time may be adapted, e.g.,automatically or based on user programming, to the patient's habits,such as a habitual sleep/wake schedule. Based on the determination thatthe period of time during which the activity state of the patient isexpected to satisfy the at least one activity criterion has elapsed, theprocessing circuitry may increase a frequency at which an activity stateof the patient is determined in order to identify or approximate thetime at which the activity state of the patient ceases to satisfy the atleast one inactivity criterion (e.g., awakens from a sleep state andarises). Identifying or approximating the time at which the activitystate of the patient ceases to satisfy the at least one inactivitycriterion may enable the processing circuitry to identify or approximatethe beginning of the predetermined period of time and determine thesecond value of the at least one HBV metric within the predeterminedperiod of time.

In any such examples, the processing circuitry may determine adifference between the first value of the at least one HBV metric andthe second value of the at least one HBV metric and determine the heartfailure status of the patient based on the difference. The processingcircuitry may determine the heart failure status of the patient based onthe difference by determining whether the difference satisfies an HBVdifference threshold value that is associated with a change in the heartfailure status of the patient.

The HBV threshold difference value may be associated with a lower end ora higher end of an HBV difference range. An HBV difference range mayrepresent a range of values of the difference between the first andsecond values of the at least one HBV metric that are associated with abaseline heart failure status of the patient (e.g., a state in which aheart failure condition of the patient is adequately compensated and/orstable). A value near the lower end of such a range may be associatedwith a different heart failure status of the patient than a value nearthe higher end of such a range. For example, a value of the differencebetween the first and second values of the HBV metric that satisfies athreshold value associated with a higher end of the HBV difference rangemay be indicative of worsening heart failure and/or increasing risk oftachyarrhythmia or bradyarrhythmia. Thus, in some examples, thetechniques for determining a heart failure status of a patient describedherein may include determining a status of one or more other aspects ofcardiac function of the patient, such as an arrhythmia-prone status ofthe patient. A value of the difference between the first and secondvalues of the HBV metric that does not satisfy a threshold valueassociated with a lower end of the HBV difference range may beindicative of an advanced state of heart failure. Thus, in someexamples, determining a heart failure status of the patient may includecomparing the difference between the first and second values of the atleast one HBV metric to more than one HBV threshold difference value.

In some examples, an HBV threshold difference value may be apatient-specific HBV threshold difference value. The patient-specificHBV threshold difference value may be periodically updated, such as totrack trends in the difference between the first and second values ofthe at least one HBV metric over multiple activity cycles of thepatient. An activity cycle of the patient may be a period of time inwhich the processing circuitry both determines that the activity stateof the patient satisfies the at least one inactivity criterion anddetermines that the activity state of the patient no longer satisfiesthe at least one inactivity criterion, such as a period of one day. Insome such examples, the HBV difference threshold value may be based onone or more values of the difference between the first and second valuesof the at least one HBV metric that correspond to one or more previousactivity cycles of the patient.

The processing circuitry may update the HBV difference threshold valuebased on a determination that a predetermined number of activity cycleshave elapsed. For example, if the processing circuitry determines thatvalues of the difference between the first and second values of the atleast one HBV metric is trending upward or downward over one or morepast activity cycles (e.g., days), the processing circuitry may updatethe HBV difference threshold by modifying the HBV difference thresholdvalue. For example, the processing circuitry may lower HBV thresholddifference value that is associated with a lower end of an HBVdifference range if the difference between the first and second valuesof the at least one HBV metric is trending downward, thereby increasingthe significance of any further decrease in the difference between thefirst and second values of the at least one HBV metric that may occur insubsequent activity cycles.

In some other examples, instead of determining a heart failure status ofthe patient based on the difference between the first and second valuesof the at least one HBV metric, the processing circuitry may beconfigured to determine the heart failure status of a patient bydetermining a current value of at least one HBV metric while theactivity state of the patient no longer satisfies the at least one theinactivity criterion, comparing the current value of the at least oneHBV metric to a baseline value of the at least one HBV metric, anddetermining the heart failure status of the patient based on thecomparison. In some such examples, the processing circuitry may comparethe current value of the at least one HBV metric to the baseline valueof the at least one HBV metric by determining whether a differencebetween the current value of the at least one HBV metric and thebaseline value of the at least one HBV metric satisfies at least onecorresponding HBV threshold value that is associated with a change inthe heart failure status of the patient. However, other aspects of suchexample techniques may be substantially similar to corresponding aspectsof example techniques in which the processing circuitry is configured todetermine the heart failure status of the patient based on thedifference between the first and second values of the at least one HBVmetric.

In some examples, the medical device may be an implantable medicaldevice (IMD) configured for implantation within the patient. The IMD mayinclude a housing, configured for subcutaneous implantation, on whichthe one or more sensors are positioned. In some examples, the IMD may bea leadless 1 MB. In other examples, the medical device may be one ormore other implanted or external devices or servers. Examples of the oneor more other implanted or external devices may include an implanted,multi-channel cardiac pacemaker, implantable cardioverter-defibrillator(ICD), implantable pulse generator (IPG), leadless (e.g., intracardiac)pacemaker, extravascular pacemaker and/or ICD, or other 1 MB orcombination of such IMDs, an external monitor, or a drug pump.

In any such examples, the processing circuitry may transmit the heartfailure status of the patient to a remote computer, receive instructionsfrom the remote computer for a medical intervention based on the heartfailure status of the patient, and transmit the instructions for themedical intervention to a user interface. Such instructions for amedical intervention may include at least one of a change in a drugselection, a change in a drug dosage, instructions to schedule a visitwith a clinician, or instructions for the patient to seek medicalattention. In this manner, a patient's diagnoses and/or treatment for aheart failure condition may be modified as needed in between clinicvisits, which may help avoid adverse medical events such as recurrentsymptoms, acute decompensation, or hospitalization.

Although a heart failure status of a patient may be determined based onresults of diagnostic or other evaluative procedures (e.g., examinationof a cardiac electrogram, blood tests, stress tests, or others), suchother techniques for determining heart failure status may require aclinician visit and thus may only be performed infrequently, such as atintervals of one or more weeks or months. Thus, such other techniquesmay not enable early detection of changes in such physiologicalfunctions before the changes lead to adverse medical events.

Changes in values of the at least one HBV metric associated with changesin patient activity, such as changes that occur soon after the patientawakens from a sleep state, may provide information regarding the heartfailure status of the patient not provided by other techniques. Forexample, a patient's vascular tone may be expected to increase withinabout 30 minutes after awakening and arising. The increase in vasculartone may reflect higher epinephrine blood levels, which may precipitateadverse medical events. Studies on circadian patterns suggest thatchanges in vascular tone occurring during the period of about 30 minutesafter an increase in patient activity (e.g., after awakening) mayreflect changes in the balance between sympathetic activity and vagaltone) and may be identified based on changes in values of at least oneHBV metric. Changes in the balance between sympathetic activity andvagal tone, occurring either during one activity cycle or acrossmultiple activity cycles, may be associated with changes in a heartfailure condition of the patient. Although monitoring changes in valuesof at least one HBV metric associated with changes in patient activitythus may enable accurate and/or efficient monitoring of changes in aheart failure status of a patient, such techniques may not readily becarried out during clinician visits. For example, it may be impracticalto monitor values of at least one HBV metric for a period of timeencompassing both an activity state of the patient that satisfies atleast one inactivity criterion and an activity state of the patient thatno longer satisfies the at least one inactivity criterion.

In some examples, the techniques described herein may enableidentification of changes in a heart failure status of a patient beforethe changes lead to symptoms, acute decompensation, and/or theprogression or the patient's heart failure condition or development ofone or more additional medical conditions. Thus, the techniquesdescribed herein may help enable determination of possibility that thepatient will experience an adverse medical event, which may helpclinicians prescribe personalized treatment to help averthospitalizations, improve clinical outcomes, and/or reduce the economicburden on the health care system.

FIG. 1 illustrates the environment of an example medical device system 2in conjunction with a patient 4 and a heart 6, in accordance with anapparatus and method of certain examples described herein. The exampletechniques may be used with an IMD 10, which may be leadless and inwireless communication with external device 12, as illustrated in FIG. 1. In some examples, IMD 10 may be coupled to one or more leads. In someexamples, IMD 10 may be implanted outside of a thoracic cavity ofpatient 4 (e.g., subcutaneously in the pectoral location illustrated inFIG. 1 ). IMD 10 may be positioned near the sternum near and/or justbelow the level of heart 6.

In some examples, IMD 10 may take the form of a Reveal LINQ™ InsertableCardiac Monitor (ICM), available from Medtronic plc, of Dublin, Ireland.External device 12 may be a computing device configured for use insettings such as a home, clinic, or hospital, and may further beconfigured to communicate with IMD 10 via wireless telemetry. Forexample, external device 12 may be coupled to a remote patientmonitoring system, such as Carelink®, available from Medtronic plc, ofDublin, Ireland. External device 12 may, in some examples, comprise aprogrammer, an external monitor, or a consumer device such as a smartphone or tablet. In other examples, the example techniques and systemsdescribed herein may be used with an external medical device in additionto, or instead of IMD 10. Such an external medical device may bepositioned externally to patient 4 (e.g., positioned on the skin ofpatient 4) and may carry out any or all of the functions describedherein with respect to IMD 10.

Medical device system 2 may include one or more sensors (e.g., forsensing an activity state of patient 4 and/or cardiac function ofpatient 4). The one or more sensors collectively may detect at least onefirst signal and at least one second signal that enable a processingcircuitry of medical device system 2 to determine whether an activitystate of patient 4 satisfies at least one inactivity criterion anddetermine values of at least one HBV metric based on such signals.Although such processing circuitry may be contained within IMD 10 and/orwithin another medical device of medical device system 2, e.g., externaldevice 12, the processing circuitry may be described herein as being acomponent of IMD 10 for the sake of clarity.

In some examples, the one or more sensors may include one or moreaccelerometers or other sensors configured to detect the at least onefirst signal, which may be at least one signal indicative of one or moreaspects of an activity state of patient 4, such as activity level,posture, and/or respiration rate. As discussed in further detail belowwith respect to FIGS. 3-4B, such one or more accelerometers may beenclosed within a housing of IMD 10. The one or more accelerometers maycomprise one or more three-axis accelerometers and may be a component ofIMD 10 or a component of another medical device of medical device system2. Signals generated by such sensors may be indicative of whether anactivity state of patient 4 satisfies at least one inactivity criterion,such as patient activity level, patient posture (e.g., lying down orupright), or a patient respiration rate.

In some examples, the one or more sensors may include a plurality ofelectrodes, which may be positioned on the housing of IMD 10. Theplurality of electrodes may be configured to detect the at least onesecond signal, which may be a cardiac electrogram. The processingcircuitry may determine the values of the at least one HBV metric basedon the at least one second signal. An HBV metric may be a measure ofvariability within a set of values of a parameter of patient 4 collectedduring a measurement period, on which basis the processing circuitry maydetermine the value of the HBV metric of patient 4. For example, theprocessing circuitry may determine a first, second, current, baseline,or other value of an HBV metric of patient 4 by determining a differencebetween each value and a subsequent value collected during themeasurement period and averaging or otherwise analyzing the differencesto determine the value of the HBV metric of patient 4. In some examples,the HBV metric may be a corresponding at least one of a T-wave alternansmetric, a PR duration metric, a short-term variability metric, or aphase-rectified signal averaging metric, based on the at least onesecond signal received from the plurality of electrodes. One or moresuch HBV metrics also may be a heart rate variability (HRV) metric, suchas a PR duration or a short-term variability metric, although in otherexamples such HBV metrics may not necessarily be an HRV metric.

The processing circuitry may determine a difference between a firstvalue of the at least one HBV metric determined while the activity stateof patient 4 satisfies at least one inactivity criterion and a secondvalue of the at least one HBV metric determined while the activity stateof patient 4 no longer satisfies the inactivity criterion. In someexamples, the processing circuitry may determine a difference between acurrent value of at least one HBV metric determined after determining anactivity state of patient 4 has increased and a baseline value of the atleast one HBV metric. The processing circuitry then may determine aheart failure status of patient 4 based on the difference between thefirst and second values of the at least one HBV metric or the comparisonof the current value of the at least one HBV metric and the baselinevalue of the at least one HBV metric.

In any such examples, processing circuitry of medical device system 2may transmit a determined heart failure status of patient 4 to a remotecomputer (e.g., external device 12). The processing circuitry then mayreceive instructions for a medical intervention from the remote computerbased on the heart failure status of patient 4 and transmit theinstructions for the medical intervention to a user interface.

In some examples, an interval at which processing circuitry of medicaldevice system 2 determines a heart failure status of patient 4 is thesame as an interval at which the processing circuitry transmits theheart failure status to a remote computer. In other examples, theprocessing circuitry may determine a heart failure status of patient 4more frequently than the processing circuitry transmits a heart failurestatus to the remote computer. By determining a heart failure statusmore often than a heart failure status is transmitted, an accuracy of atechnique for determining a heart failure status may be enhanced byeliminating outlier measurements. For example, the processing circuitrymay determine that a difference between first and second values of atleast one HBV metric or a difference between a current value of at leastone HBV metric and a corresponding baseline value of the at least oneHBV metric satisfies a threshold only if a certain number or proportionof preceding results satisfied the threshold. In other examples, asingle incident in which such a difference satisfied a threshold maysuffice to cause the processing circuitry to determine that a change inthe heart failure status of patient 4 has occurred.

In some examples, a clinician may configure a sensitivity of theprocessing circuitry to different threshold values at or after the timeof implant of IMD 10, depending on factors such as the individualcondition of patient 4 (e.g., a medical history of patient 4) and/orclinical data for a patient population having one or morecharacteristics in common with patient 4. For example, a clinician mayconfigure a sensitivity of the processing circuitry based on a stage ofa heart failure condition of patient 4 and/or an arrhythmia history ofpatient 4. In examples in which a technique includes comparing adifference between first and second values of at least one HBV metric ora difference between a current value of at least one HBV metric an d acorresponding baseline value to more than one threshold value (e.g., afirst threshold value associated with a lower end of a range and asecond threshold value associated with a lower end of a range), aclinician may configure the processing circuitry to be more sensitive tovalues that satisfy the first threshold value. For example, theprocessing circuitry may determine that the heart failure status ofpatient 4 has changed if a difference between first and second values ofat least one HBV metric or a difference between a current value of atleast one HBV metric and a corresponding baseline value satisfies one ofthe first threshold value or the second threshold value fewer times thanmay be required for the other threshold value, such as depending onwhich threshold, if satisfied, may be more predictive of an adversemedical event for patient 4. As discussed below, several aspects of theoperation of IMD 10 may be configured by a clinician to help achieveimproved monitoring and clinical outcomes for individual patients suchas patient 4.

In some examples, a clinician may configure the processing circuitry toutilize different threshold values or otherwise adjust the sensitivityof the detection of the heart failure status based on one or more othercriteria, such as an occupational schedule of patient 4 in examples inwhich patient 4 is engaged in an occupation. For example, changes in aheart failure status of patient 4 may be more likely to occur duringworkdays than during non-workdays. In some examples, changes in a heartfailure status of patient 4 may be more likely to occur on a first dayof a period of multiple workdays (e.g., a first workday of a workweek).This phenomenon may be associated with increases in stress levels and/orphysical exertion experienced by patient 4 on workdays relative tonon-workdays and on a first workday of a workweek relative to subsequentworkdays. In some such examples, a clinician may configure a sensitivitywith which the processing circuitry identifies changes in heart failurestatus by setting one or more threshold values for a workday of patient4 that differ from one or more corresponding threshold valuesnon-workdays of patient 4 and/or from other workdays of a period ofmultiple workdays. Additionally, or alternatively, the clinician mayconfigure the processing circuitry to be more sensitive to values thatsatisfy a first threshold value than values that satisfy a secondthreshold value, or vice versa, on workdays (i.e., workdays in generalor a particular workday) of patient 4 than on non-workdays or otherworkdays of patient 4. In this manner, the sensitivity of processingcircuitry of IMD 10 may be adapted to account for times when a heartfailure status of patient 4 may be more likely to change.

In some examples, IMD 10 may be configured to undertake a learning phaseafter implantation into patient 4. During such a learning phase, theprocessing circuitry may determine one or more baseline values and oneor more threshold values, which the processing circuitry may store in amemory of IMD 10 or other device of medical device system 2. Forexample, the processing circuitry may determine a baseline differencebetween a first value of the at least one HBV metric determined whilethe activity state of patient 4 satisfies at least one inactivitycriterion (e.g., during a sleep state) and a second value of the atleast one HBV metric determined while the activity state of patient 4 nolonger satisfies the inactivity criterion (e.g., soon after waking). Forexample, IMD 10 may determine differences between the first and secondvalues of the at least one HBV metric during a plurality of activitycycles over a period of time (e.g., a week or more) to determine abaseline difference value during a period when the condition of patient4 is stable and not decompensating. In some examples, the processingcircuitry may determine the baseline difference value by averaging orotherwise combining the differences between the first and second valuesof the at least one HBV metric during the plurality of activity cyclesof patient 4. Based on the determined baseline difference value betweenthe first and second values of the at least one HBV metric, theprocessing circuitry or a clinician may determine an HBV differencethreshold value. An HBV difference threshold value may be an HBVdifference value that is greater than or less than the baselinedifference value by a predetermined amount indicative of a change in theheart failure status of patient 4.

In examples in which the processing circuitry determines the heartfailure status of patient 4 based on a difference between a currentvalue of at least one HBV metric determined within a predeterminedperiod of time after determining that an activity state of patient 4 hasincreased (e.g., soon after waking) and a corresponding baseline value,the processing circuitry similarly may determine the baseline value ofthe at least one HBV metric during a plurality of activity cycles. Insuch examples, the processing circuitry or a clinician may determine anHBV threshold value, which may be an HBV value that is greater than orless than the baseline difference value by a predetermined amountindicative of a change in the heart failure status of patient 4. In someexamples, the processing circuitry may determine the baseline differencevalue by averaging or otherwise combining the differences between thefirst and second values of the at least one HBV metric during theplurality of activity cycles of patient 4.

In any such examples, the processing circuitry may determine thebaseline values by averaging values collected during the trainingperiod, although the processing circuitry may use other methods ofdetermining baseline values from collected values. In some examples, theprocessing circuitry may reject outlier values collected during thetraining period prior to determining the baseline values based on theremaining collected values. In this manner, a baseline value of at leastone HBV metric may be based on a relatively large group of past valuesof the at least one HBV metric of patient 4. In some examples, theprocessing circuitry may determine values of at least one HBV metric ofpatient 4 that are compared corresponding baseline value, eitherdirectly or by comparison to a threshold value based on a baselinevalue, based on a relatively smaller group of values of the at least oneHBV metric. For example, the processing circuitry may determine a firstvalue, a second value, and/or a current value of at least one HBV metricof patient 4 based on a short-term average of a relatively smaller groupof recent values of the at least one HBV metric occurring subsequent tothe activity cycles on which a corresponding baseline value is based.Thus, in some examples, the processing circuitry may determine one ormore values of an HBV metric by averaging or otherwise combining a groupof such values.

Because heart failure conditions may be progressive in nature, baselineand/or threshold values associated with patient 4 may be updatedperiodically. For example, IMD 10 may undertake a new learning phasemonthly, quarterly, yearly, or at an expiration of any other appropriateperiod. The new learning phase may produce new values associated withone or more baseline and/or threshold values described with respect tothe techniques described herein, based on an updated heart failurestatus of patient 4. In other examples, a clinician may program IMD 10to update such values as needed, such as following a health eventexperienced by patient 4 that may affect the applicability of suchvalues to one or more aspects of the heart failure status of patient 4.

In addition to or instead of undertaking a new learning phase todetermine one or more updated threshold values, the processing circuitrymay determine one or more threshold values based on trends in determinedvalues of one or more HBV metrics of patient 4. Determining one or morethreshold values based on such trends may help enable detection ofadditional aspects of changes in a heart failure state of patient 4. Asdiscussed above, a difference between first and second values of atleast one HBV metric or a difference between current and baseline valuesof at least one HBV metric that satisfies a threshold value associatedwith a higher end of an HBV difference range may be indicative ofworsening heart failure and/or increasing risk of tachyarrhythmia orbradyarrhythmia, whereas such a difference that does not satisfy athreshold value associated with a lower end of an HBV difference rangemay be indicative of an advanced state of heart failure. However,fluctuations in such differences that satisfy a threshold valueassociated with a lower end of an HBV difference range and do notsatisfy a threshold value associated with a higher end of an HBVdifference range nonetheless may be indicative of changes in heartfailure status.

For example, an increase in an absolute value of such fluctuationsacross one or more previous activity cycles of patient 4 may beindicative of increasingly irregular cardiac function of patient 4.Additionally, or alternatively, fluctuations in such differences thatoccasionally do not satisfy a threshold value associated with a lowerend of an HBV difference range or that occasionally satisfy a thresholdvalue associated with a higher end of an HBV difference range may beindicative of increasingly irregular cardiac function of patient 4.Increasingly irregular cardiac function, which may be associated withincreased irregularity in the balance between sympathetic andparasympathetic nervous system activity, may indicate a worsening heartfailure condition of patient 4.

Thus, in some examples, the processing circuitry may determine an HBVdifference threshold value or HBV threshold value based on fluctuationsin a difference between first and second values of at least one HBVmetric, or fluctuations in current values of at least one HBV metric,occurring across one or more previous activity cycles of patient 4. Inexamples in which the processing circuitry determines the heart failurestatus of patient 4 based on a comparison of a difference between firstand second values of at least one HBV metric to an HBV differencethreshold, the HBV difference threshold value may be based on one ormore values of the difference between the first and second values of theat least one HBV metric that correspond to one or more previous activitycycles of patient 4. In examples in which the processing circuitrydetermines the heart failure status of patient 4 based on a comparisonof a difference between current and baseline values of at least one HBVmetric to at least one corresponding HBV threshold, the predeterminedperiod of time may be a current predetermined period of time, and atleast one corresponding HBV threshold value may be based on one or morecorresponding previous values of the at least one HBV metric of patient4 that correspond to one or more previous predetermined periods of time.

In any such examples, if the one or more previous activity cycles ofpatient 4 indicate increasingly irregular cardiac function, theprocessing circuitry may modify a threshold value to provide greatersensitivity to continued fluctuation, such as by raising a thresholdvalue associated with a lower end of an HBV difference range or loweringa threshold value associated with a higher end of an HBV differencerange. In this manner, the processing circuitry may modify thesensitivity of a technique for determining heart failure status byaccounting for trends in in determined values of one or more HBV metricsof patient 4, which may further help enable detection of changes in aheart failure status of patient 4.

Thus, as described above, the operating parameters of IMD 10 readily maybe customized to meet the needs of patient 4, such as by settingbaseline and/or threshold values based on the individual attributes ofpatient 4, such as a heart failure condition or other medical conditionof patient and/or an existing medication regimen of patient 4. Theextent and ease of customizability of IMD 10 may provide numerousbenefits. For example, customizability of IMD 10 to reflect a heartfailure condition or existing medication regimen of patient 4 helpsensure that appropriate therapies are prescribed for patient 4, therebyreducing a possibility of human error in prescribing treatment. Inaddition, in examples in which the processing circuitry (e.g., of IMD10) one or more baseline and/or threshold values for patient 4, burdenson the clinician's time may be reduced, which may reduce the time neededfor an office visit and promote efficient treatment. Moreover, asdiscussed above, techniques for using medical device system 2 todetermine a heart failure status of patient 4 between clinician visitsmay help avoid adverse medical events, which may lead to better clinicaloutcomes such as improved quality of life for patient 4 or reducedmedical expenses.

External device 12 may be a computing device (e.g., used in a home,ambulatory, clinic, or hospital setting) to communicate with IMD 10 viawireless telemetry. External device 12 may include or be coupled to aremote patient monitoring system, such as Carelink®, available fromMedtronic plc, of Dublin, Ireland. External device 12 may be, as anexample, a programmer, external monitor, or a consumer device (e.g., asmart phone). In some examples, external device 12 may receive data,alerts, patient physiological information, or other information from IMD10.

External device 12 may be used to program commands or operatingparameters into IMD 10 for controlling its functioning (e.g., whenconfigured as a programmer for IMD 10). In some examples, externaldevice 12 may be used to interrogate IMD 10 to retrieve data, includingdevice operational data as well as physiological data accumulated in IMDmemory. Such interrogation may occur automatically according to aschedule and/or may occur in response to a remote or local user command.Programmers, external monitors, and consumer devices are examples ofexternal devices 12 that may be used to interrogate IMD 10. Examples ofcommunication techniques used by IMD 10 and external device 12 includeradiofrequency (RF) telemetry, which may be an RF link established viaBluetooth, WiFi, or medical implant communication service (MICS). Insome examples, external device 12 may include a user interfaceconfigured to allow patient 4, a clinician, or another user to remotelyinteract with IMD 10. In some such examples, external device 12, and/orany other device of medical device system 2, may be a wearable device,(e.g., in the form of a watch, necklace, or other wearable item). Suchwearable devices may include one or more electrodes or other sensorsconfigured for sensing signals used in determining values of at leastone HBV metric in accordance with the techniques described herein.

Medical device system 2 is an example of a medical device systemconfigured to monitor a heart failure status of patient 4 and facilitateupdates to patient 4's treatment (e.g., for a heart failure condition)as needed between clinician visits. The techniques described herein maybe performed by processing circuitry of a device of medical devicesystem 2, such as processing circuitry of IMD 10. Additionally, oralternatively, the techniques described herein may be performed, inwhole or in part, by processing circuitry of external device 12, and/orby processing circuitry of one or more other implanted or externaldevices or servers (not shown). Examples of the one or more otherimplanted or external devices may include an implanted, multi-channelcardiac pacemaker, ICD, IPG, leadless (e.g., intracardiac) pacemaker,extravascular pacemaker and/or ICD, or other IMD or combination of suchIMDs configured to deliver CRT to heart 6, an external monitor, or adrug pump.

Communication circuitry of each of the devices of medical device system2 (e.g., IMD 10 and external device 12) may enable the devices tocommunicate with one another. In addition, although one or more sensors(e.g., electrodes) are described herein as being positioned on a housingof IMD 10, in other examples, such sensors may be positioned on ahousing of another device implanted in or external to patient 4. In suchexamples, one or more of the other devices may include processingcircuitry configured to receive signals from the electrodes or othersensors on the respective devices and/or communication circuitryconfigured to transmit the signals from the electrodes or other sensorsto another device (e.g., external device 12) or server.

FIGS. 2-4B illustrate various aspects and example arrangements of IMD 10of FIG. 1 . For example, FIG. 2 conceptually illustrates an examplephysical configuration of IMD 10. FIG. 3 is a block diagram illustratingan example functional configuration of IMD 10. FIGS. 4A and 4Billustrate additional views of an example physical and functionalconfiguration of IMD 10. It should be understood that any of theexamples of IMD 10 described below with respect to FIGS. 2-4B may beused to implement the techniques described herein for determining aheart failure status of patient 4.

FIG. 2 is a conceptual drawing illustrating an example configuration ofIMD 10 of FIG. 1 . In the example shown in FIG. 2 , IMD 10 may comprisea leadless, subcutaneously-implantable monitoring device having ahousing 14, a proximal electrode 16A, and a distal electrode 16B.Housing 14 encloses electronic circuitry located inside the IMD 10, andprotects the circuitry contained therein from fluids such as bodyfluids. In some examples, 14 may comprise first major surface 18, secondmajor surface 20, proximal end 22, and distal end 24. Proximal electrode16A and distal electrode 16B may be positioned near respective proximaland distal ends 22 and 24 of IMD 10, such that a spacing betweenproximal electrode 16A and distal electrode 16B may range from about30-55 mm, about 35-55 mm, or about 40-55 mm, or more generally fromabout 25-60 mm. In some examples, IMD 10 may include one or moreadditional electrodes and/or one or more other sensors (not shown),which may be positioned on one or both of major surfaces 18, 20 of IMD10. In any such examples, electrical feedthroughs may provide electricalconnection of electrodes 16A, 16B or other sensors to circuitry withinhousing 14.

In the example shown in FIG. 2 , IMD 10 is defined by a length L, awidth W, and thickness or depth D. In this example, IMD 10 is in theform of an elongated rectangular prism in which length L issignificantly greater than width W, and in which width W is greater thandepth D. However, other configurations of IMD 10 are contemplated, suchas those in which the relative proportions of length L, width W, anddepth D vary from those described and shown in FIG. 2 . In someexamples, the geometry of the IMD 10, such as the width W being greaterthan the depth D, may be selected to allow IMD 10 to be inserted underthe skin of the patient using a minimally invasive procedure and toremain in the desired orientation during insertion. In addition, IMD 10may include radial asymmetries (e.g., the rectangular shape) along alongitudinal axis of IMD 10, which may help maintain the device in adesired orientation following implantation.

Overall, IMD 10 may have a length L of about 20-30 mm, about 40-60 mm,or about 45-60 mm. In some examples, the width W of first major surface18 may range from about 3-10 mm, and may be any single width or range ofwidths between about 3-10 mm. In some examples, a depth D of IMD 10 mayrange from about 2-9 mm. In other examples, the depth D of IMD 10 mayrange from about 2-5 mm, and may be any single or range of depths fromabout 2-9 mm. In any such examples, IMD 10 is sufficiently compact to beimplanted within the subcutaneous space of patient 4 in the region of apectoral muscle.

IMD 10 may have a geometry and size designed for ease of implantationand patient comfort. For example, IMD 10 may have a volume of 3 cubiccentimeters (cm³) or less, 1.5 cm³ or less, or any volume therebetween.As illustrated in FIG. 2 , proximal end 22 and distal end 24 may berounded, which may reduce discomfort and/or irritation to surroundingtissue when IMD 10 implanted under the skin of patient 4. An exampleconfiguration of IMD 10, including an instrument and method forinserting IMD 10 is described in U.S. Patent Publication No.2014/0276928, incorporated herein by reference in its entirety. Anexample configuration of IMD 10 also is described in U.S. PatentPublication No. 2016/0310031, incorporated herein by reference in itsentirety.

In some examples, IMD 10 may be configured for implantation withinpatient 4 such that first major surface 18 of IMD 10 faces outwardtowards the skin when IMD 10 is inserted within patient 4 and secondmajor surface 20 is faces inward toward musculature of patient 4. Firstand second major surfaces 18, 20 may face in directions along a sagittalaxis of patient 4, as illustrated in FIG. 1 , and this orientation maybe maintained upon implantation due to the dimensions of IMD 10.Additionally, or alternatively, IMD 10 may be configured forimplantation within patient 4 in one or more other orientations relativeto one or more anatomical landmarks of patient 4.

In the example shown in FIG. 2 , proximal end 22 of IMD 10 includesheader assembly 32 having one or more of integrated antenna 26,anti-migration projections 34, and suture hole 36. Integrated antenna 26is located on the same major surface (e.g., first major surface 18) aselectrode 16A, and may be an integral part of header assembly 32. Inother examples, integrated antenna 26 may be formed on the major surfaceopposite from electrode 16A or may be incorporated within housing 14 ofIMD 10. Antenna 26 may be configured to transmit or receiveelectromagnetic signals for communication. For example, antenna 26 maybe configured to transmit to or receive signals from a programmer viainductive coupling, electromagnetic coupling, tissue conductance, NearField Communication (NFC), Radio Frequency Identification (RFID),Bluetooth, WiFi, or other proprietary or non-proprietary wirelesstelemetry communication schemes. Antenna 26 may be coupled tocommunication circuitry of IMD 10, which may drive antenna 26 totransmit signals to external device 12. Antenna 26 may transmit signalsreceived from external device 12 to processing circuitry of IMD 10 viathe communication circuitry.

IMD 10 may include several features for retaining IMD 10 in positiononce subcutaneously implanted in patient 4. For example, as shown inFIG. 2 , housing 14 may include anti-migration projections 34 positionedadjacent integrated antenna 26. Anti-migration projections 34 maycomprise a plurality of bumps or protrusions extending away from firstmajor surface 18 and may reduce or prevent movement of IMD 10 afterimplantation in patient 4. In other examples, anti-migration projections34 may be located on the opposite major surface as proximal electrode16A and/or integrated antenna 26. In addition to or instead ofanti-migration projections 34, a portion of housing 14 (e.g., headerassembly 32) may define a suture hole 36, which may enable a clinicianto suture IMD 10 to patient tissue to reduce or prevent movement of IMD10 after implantation in patient 4. In the example of FIG. 2 , suturehole 36 is defined by a portion of header assembly 32 adjacent toproximal electrode 16A. In some examples, header assembly 32 maycomprise a molded header assembly made from a polymeric material, whichmay be integral with or separable from the main portion of IMD 10.

In examples in which the processing circuitry of medical device system 2is configured to determine values of at least one HBV metric based on acardiac electrogram signal, IMD 10 may include a plurality ofelectrodes. For example, as illustrated in FIG. 2 , IMD 10 may include aproximal electrode 16A and a distal electrode 16B. As shown in theillustrated example, proximal electrode 16A may be positioned on headerassembly 32, and distal electrode 16B may be formed from an uninsulateddistal portion of conductive housing 14. Proximal electrode 16A anddistal electrode 16B may be positioned near respective proximal anddistal ends 22 and 24 of IMD 10, such that a spacing between proximalelectrode 16A and distal electrode 16B may range from about 30-55 mm,about 35-55 mm, or about 40-55 mm, or more generally from about 25-60mm. In some examples, IMD 10 also may include one or more additionalelectrodes (not shown) positioned on one or both of major surfaces 18,20 of IMD 10. In any such examples, electrical feedthroughs may provideelectrical connection of electrodes 16A, 16B, any additional electrodes,and antenna 26, to circuitry within housing 14.

In the example shown in FIG. 2 , proximal electrode 16A is in closeproximity to proximal end 22, and distal electrode 16B is in closeproximity to distal end 24 of IMD 10. In this example, distal electrode16B is not limited to a flattened, outward facing surface, but mayextend from first major surface 18, around rounded edges 28 or endsurface 30, and onto the second major surface 20 in a three-dimensionalcurved configuration. As illustrated, proximal electrode 16A is locatedon first major surface 18 and is substantially flat and outward facing.However, in other examples not shown here, proximal electrode 16A anddistal electrode 16B both may be configured like proximal electrode 16Ashown in FIG. 2 , or both may be configured like distal electrode 16Bshown in FIG. 2 . Any of electrodes 16A, 16B may be formed of abiocompatible conductive material. For example, any of electrodes 16A,16B may be formed from any of stainless steel, titanium, platinum,iridium, or alloys thereof. In addition, electrodes of IMD 10 may becoated with a material such as titanium nitride or fractal titaniumnitride, although other suitable materials and coatings for suchelectrodes may be used.

Proximal electrode 16 and distal electrode 16B may be used to sensecardiac electrogram signals when IMD 10 is implanted subcutaneously inpatient 4. In the techniques described herein, processing circuitry ofIMD 10 may determine values of at least one HBV metric based on cardiacelectrogram signals. In some examples, the processing circuitry also maydetermine whether cardiac electrogram signals of patient 4 areindicative of arrhythmia (e.g., the presence or absence of atrialfibrillation and a ventricular rate during atrial fibrillation) or otherabnormalities, which the processing circuitry may evaluate indetermining whether a cardiac function of patient 4 has changed.

For example, the processing circuitry may determine that the cardiacfunction of patient 4 has changed based on determining the presence ofatrial fibrillation. Thus, the presence of atrial fibrillation, alone orin combination with values of at least one HBV metric, may warrant anupdated diagnosis of a heart failure condition of patient 4. In suchexamples, instructions for a medical intervention that an externaldevice (e.g., external device 12) may transmit to a user device may bebased, at least in part, on the determination of atrial fibrillation.Additionally, or alternatively, the processing circuitry may treat thepresence of atrial fibrillation as noise in the cardiac electrogramsignal when determining the values of the at least one HBV metric. Insome such examples, the processing circuitry may discard HBV valuesdetermined during an episode of atrial fibrillation, or apply filteringor other data or signal processing techniques to mitigate the influenceof the atrial fibrillation on the determined HBV values. In any suchexamples, the cardiac electrogram signals may be stored in a memory ofthe IMD 10, and data derived from the cardiac electrogram signals may betransmitted via integrated antenna 26 to another medical device, such asexternal device 12.

In some examples, IMD 10 may include one or more additional sensors,such as one or more accelerometers, microphones, and/or pressure sensors(not shown). Such accelerometers may be 3D accelerometers configured togenerate signals indicative of one or more types of movement of thepatient, such as gross body movement (e.g., activity) of the patient,patient posture, movements and/or sounds associated with the beating ofheart 6, respiration rate, or others. Such microphones may be configuredto generate signals indicative of sounds associated with the beating ofheart 6 and/or respiration of patient 4 sounds, and/or dyspnea, on thebasis of which the processing circuitry may determine a heart rate, arespiration rate or depth of patient 4, or other parameter associatedwith such sounds. Such pressure sensors may be configured to generatesignals indicative of changes in pressure associated with the beating ofheart 6, on the basis of which the processing circuitry may determine aheart rate of patient 4.

In some examples, the processing circuitry may determine one or moreaspects of an activity state of patient 4, such as whether the activitystate of patient 4 satisfies at least one inactivity criterion, based onat least one signal received from the one or more accelerometers and/orpressure sensors. Additionally, or alternatively, the processingcircuitry may determine one or more aspects of the activity state ofpatient 4 based on one or more signals received from electrodes ofmedical device system 2 (e.g., electrodes 16A, 16B) that may beindicative of a heart rate and/or a respiration parameter of patient 4.For example, the processing circuitry may determine whether at least oneof an activity level, posture, heart rate, body temperature, soundsassociated with dyspnea, pulmonary artery pressure, sleep/non-sleepbrain waves, or a respiration rate or depth of patient 4, and/or a timeof day, is indicative of patient 4 being substantially inactive (e.g.,asleep) or active (e.g., awake) based on the one or more signalsreceived from the one or more accelerometers, microphones, pressuresensors, other types of sensors, electrodes 16A, 16B, and/or otherelectrodes that may be included in IMD 10 or in another device ofmedical device system 2.

In examples in which at least one inactivity criterion comprises aposture of patient 4, the processing circuitry may be configured todetermine that an activity state of patient 4 satisfies the at least oneinactivity criterion by determining that patient 4 is lying down and/orasleep based on the at least one signal received from the one or moreaccelerometers. Additionally, or alternatively, the processing circuitrymay determine that patient 4 is asleep based on a heart rate of patient4, such as based on a determination that a heart rate of patient 4 is ator below a threshold value associated with a sleep state for apredetermined period of time. Thus, in some example techniques fordetermining the heart failure status of patient 4, the processingcircuitry may determine a first value of at least one HBV metric whilethe activity state of patient 4 satisfies the at least one inactivitycriterion by determining the first value of the at least one HBV metricwhile patient 4 is lying down and/or asleep.

In some such examples, the processing circuitry may determine that theactivity state of patient 4 no longer satisfies the at least oneinactivity criterion by determining that patient 4 is in an uprightposture and/or awake based on the at least one first signal.Additionally, or alternatively, the processing circuitry may determinethat patient 4 is awake based on a heart rate of patient 4, such asbased on a determination that a heart rate of patient 4 is at or above athreshold value associated with a waking state for a predeterminedperiod of time. Thus, in some example techniques for determining theheart failure status of patient 4, the processing circuitry maydetermine a second value or current value of at least one HBV metricwhile the activity state of patient 4 no longer satisfies the at leastone inactivity criterion by determining the second value or currentvalue of the at least one HBV metric while patient 4 is in the uprightposture and/or awake, within a predetermined period of time (e.g.,within about 30 minutes) after determining patient 4 is in the uprightposture and/or has awakened from a sleep state.

In some examples, the processing circuitry may determine values of oneor more HBV metrics based on signals indicative of heart rate from theone or more accelerometers, microphones, and/or pressure sensors, inaddition to or instead determining values of the one or more HBV metricsof based on a cardiac electrogram signal received from electrodes 16A,16B. In any such examples, the processing circuitry may determine avalue of an HBV metric based on a plurality of values. For example, inexample techniques in which the processing circuitry determines a heartfailure status of patient 4 based on a difference between first andsecond values of at least one HBV metric, the processing circuitry maydetermine one or both of the first value and the second value of the atleast one HBV metric by determining a corresponding representative firstand/or second value of the at least one HBV metric based on acorresponding plurality of first and/or second values of the at leastone HBV metric. Similarly, in example techniques in which the processingcircuitry determines a heart failure status of patient 4 based on adifference between a current value of at least one HBV metric and abaseline value of the at least one HBV metric, the processing circuitrymay determine one or both of the current value and the baseline value ofthe at least one HBV metric by determining a correspondingrepresentative current value of the at least one HBV metric based on acorresponding plurality of current and/or baseline values of the atleast one HBV metric.

In any such examples, the processing circuitry may determine each of aplurality of values of an HBV metric by intermittently sampling valuesof the HBV metric during a period of time. For example, the processingcircuitry may determine values of the HBV metric at 3-minute intervalsfor a 30 second period each over ten measurement cycles for a totalduration of 30 minutes, although any other suitable intervals, number ofcycles, or time periods may be used. The processing circuitry then maydetermine a representative value of the HBV metric based on the valuesof the HBV metric collected during the period of time. In some examples,the processing circuitry may reject any outlier values of the HBVmetric, which may help enhance the accuracy of the determination of theheart failure status of patient 4. The processing circuitry may averagethe collected measurements, less any rejected outlier values, todetermine the representative value, although any other suitable methodof data analysis may be used.

Although processing circuitry of IMD 10 is described above as beingconfigured to receive signals from the one or more accelerometers, oneor more microphones, one or more pressure sensors and/or electrodes 16A,16B and determine a value of one or more parameters of patient 4 basedon such signals, any steps described herein as being carried out byprocessing circuitry of IMD 10 may carried out by processing circuitryof one or more devices. For example, processing circuitry of externaldevice 12, or any other suitable implantable or external device orserver, may be configured to receive signals from the one or moreaccelerometers, one or more microphones, one or more pressure sensorsand/or electrodes 16A, 16B, such as via communication circuitry of IMD10.

FIG. 3 is a functional block diagram illustrating an exampleconfiguration of IMD 10 of FIGS. 1 and 2 . As shown in FIG. 3 , IMD 10includes processing circuitry 50 sensing circuitry 52, communicationcircuitry 54, memory 56, sensors 58, and switching circuitry 60, inaddition to previously-described electrodes 16A, 16B, one or more ofwhich may be disposed within housing 14 of IMD 10. In some examples,memory 56 includes computer-readable instructions that, when executed byprocessing circuitry 50, cause IMD 10 and processing circuitry 50 toperform various functions attributed to IMD 10 and processing circuitry50 herein. Memory 56 may include any volatile, non-volatile, magnetic,optical, or electrical media, such as a random-access memory (RAM),read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasableprogrammable ROM (EEPROM), flash memory, or any other digital media.

Processing circuitry 50 may include fixed function circuitry and/orprogrammable processing circuitry. Processing circuitry 50 may includeany one or more of a microprocessor, a controller, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or equivalent discrete or analoglogic circuitry. In some examples, processing circuitry 50 may includemultiple components, such as any combination of one or moremicroprocessors, one or more controllers, one or more DSPs, one or moreASICs, or one or more FPGAs, as well as other discrete or integratedlogic circuitry. The functions attributed to processing circuitry 50herein may be embodied as software, firmware, hardware or anycombination thereof.

In some examples, timing and/or control aspects of processing circuitry50 may comprise a dedicated hardware circuit, such as an ASIC, separatefrom other aspects of processing circuitry 50, such as a microprocessor,or a software module executed by a component of processing circuitry 50(e.g., a microprocessor or ASIC). Timing and/or control aspects ofprocessing circuitry 50 may monitor the passage of time to determinewhen a period of time has elapsed. For example, the timing and/orcontrol aspects of processing circuitry 50 may determine, prior toprocessing circuitry 50 determining that the activity state of patient 4no longer satisfies at least one inactivity criterion, that a period oftime during which the activity state of the patient is expected tosatisfy the at least one inactivity criterion (e.g., a period of timeduring which patient 4 is expected to be asleep) has elapsed and that aperiod of time during which the activity state of patient 4 is expectedto no longer satisfy the at least one inactivity criterion (e.g., aperiod of time during which patient 4 is expected to awaken) has begun.The period of time during which the activity state of patient 4 isexpected to no longer satisfy the at least one inactivity criterion mayinclude at least a portion of the predetermined period of time after anactivity state of patient 4 has increased during which processingcircuitry 50 may determine a second value or a current value of at leastone HBV metric. In some such examples in which IMD 10 includes one ormore accelerometers, processing circuitry 50 may cross-reference anindication by timing and control circuitry that a period of time haselapsed or begun with accelerometer data, such as to confirm whetherpatient 4 is asleep or awake as expected.

In some examples, processing circuitry 50 may determine the activitystate of patient 4 and/or values of at least one HBV metric morefrequently when the activity state of patient 4 is expected to no longersatisfy the at least one inactivity criterion than when patient 4 isexpected to satisfy the at least one inactivity criterion, which mayenable processing circuitry 50 to identify or approximate the time atwhich the activity state of patient 4 ceases to satisfy the at least oneinactivity criterion while conserving power during times when such closemonitoring is not needed. In some examples, timing and/or controlaspects of processing circuitry 50 may control IMD 10 to transmit aheart failure status of patient 4 to external device 12, such as at theconclusion of a monitoring interval.

Memory 56 may store determined values of one or more HBV metrics ofpatient 4 and/or one or more intervals or time periods according towhich processing circuitry 50 may determine values of one or more HBVmetrics in stored measurements/intervals 62. Memory 56 also may storebaseline and/or threshold values, which processing circuitry 50 maydetermine during a learning phase of IMD 10, in tables 64. In someexamples, processing circuitry may determine an HBV difference thresholdvalue based on a determined baseline difference between a baseline firstvalue of the at least one HBV metric and a baseline second value of theat least one HBV metric.

For example, processing circuitry 50 may receive at least one baselinesecond signal from at least electrodes of IMD 10 (e.g., electrodes 16A,16B). Processing circuitry 50 then may determine a baseline first valueof the at least one HBV metric and a baseline second value of the atleast one HBV metric based on the at least one baseline second signal.As with other first and second values of the at least one HBV metric,processing circuitry may determine the baseline first value of the atleast one HBV metric while an activity state of patient 4 satisfies atleast one inactivity criteria and determine the second baseline value ofthe at least one HBV value within a predetermined period of time ofdetermining that the activity state of patient 4 no longer satisfies theat least one inactivity criteria. Processing circuitry 50 then maydetermine a baseline difference between the baseline first value of theat least one HBV metric and the baseline second value of the at leastone HBV metric and determine the HBV difference threshold value based onthe baseline difference between the baseline first and second values ofthe at least one HBV metric.

Processing circuitry 50 similarly may determine one or more of baselineand/or threshold values in examples in which processing circuitry 50determines the heart failure status of patient 4 based on a differencebetween a current value of at least one HBV metric and a baseline valueof the at least one HBV metric. For example, processing circuitry 50 maydetermine a patient-specific baseline value of the at least one HBVmetric by receiving at least one baseline second signal from sensors 58(e.g., from at least two electrodes of IMD 10) and determine thepatient-specific value of the at least one baseline HBV metric based onthe baseline at least one second signal. In any such examples, tables 64may include pre-programmed baseline and/or threshold values that aclinician may select for patient 4 during setup of IMD 10 or manuallyenter based on the clinician's assessments of patient 4.

Sensing circuitry 52 and communication circuitry 54 may be selectivelycoupled to electrodes 16A, 16B via switching circuitry 60 as controlledby processing circuitry 50. Sensing circuitry 52 may monitor signalsfrom electrodes 16A, 16B in order to monitor electrical activity ofheart and produce a cardiac electrogram from which processing circuitry50 may determine values of a heart rate of patient 4 and/or values ofone or more HBV metrics of patient 4. In some examples in which IMD 10includes one or more accelerometers, microphones, and/or pressuresensors, sensing circuitry 52 also may monitor signals from sensors 58,which may include such accelerometers, microphones, and/or pressuresensors. In some examples, sensing circuitry 52 may include one or morefilters and amplifiers for filtering and amplifying signals receivedfrom one or more of electrodes 16A, 16B and/or other sensors 58.

In some examples, sensing circuitry 52 and/or processing circuitry 50may include a rectifier, filter and/or amplifier, a sense amplifier,comparator, and/or analog-to-digital converter. Upon receiving signalsfrom electrodes 16A, 16B and/or other sensors 58 via sensing circuitry52, processing circuitry 50 may determine values of at least one HBVmetric of patient 4. Processing circuitry 50 then may determine a heartfailure status of patient 4 based on a difference between first andsecond values of at least one HBV metric or a difference between currentand baseline values of at least one HBV metric, such as based on whethersuch a difference satisfies a corresponding HBV difference thresholdvalue or HBV threshold value stored in tables 64.

Communication circuitry 54 may include any suitable hardware, firmware,software or any combination thereof for communicating with anotherdevice, such as external device 12 or another medical device or sensor,such as a pressure sensing device. Under the control of processingcircuitry 50, communication circuitry 54 may receive downlink telemetryfrom, as well as send uplink telemetry to, external device 12 or anotherdevice with the aid of an internal or external antenna, e.g., antenna26. In some examples, communication circuitry 54 may communicate withexternal device 12. In addition, processing circuitry 50 may communicatewith a networked computing device via an external device (e.g., externaldevice 12) and a computer network, such as the Medtronic CareLink®Network developed by Medtronic, plc, of Dublin, Ireland.

A clinician or other user may retrieve data from IMD 10 using externaldevice 12, or by using another local or networked computing deviceconfigured to communicate with processing circuitry 50 via communicationcircuitry 54. The clinician may also program parameters of IMD 10 usingexternal device 12 or another local or networked computing device. Insome examples, the clinician may select one or more baseline and/orthreshold values associated with one or more HBV metrics, times of dayduring which patient 4 is expected to be awake or asleep, predeterminedperiods of time, a number of measurements to be completed during aperiod, or other parameters of IMD 10.

One or more components of IMD 10 may be coupled a power source, whichmay include a rechargeable or non-rechargeable battery positioned withinhousing 14 of IMD 10. A non-rechargeable battery may be selected to lastfor several years, while a rechargeable battery may be inductivelycharged from an external device, e.g., on a daily or weekly basis.

FIGS. 4A and 4B illustrate two additional example IMDs that may besubstantially similar to IMD 10 of FIGS. 1-3 , but which may include oneor more additional features. The components of FIGS. 4A and 4B may notnecessarily be drawn to scale, but instead may be enlarged to showdetail. FIG. 4A is a block diagram of a top view of an exampleconfiguration of an IMD 10A. FIG. 4B is a block diagram of a side viewof example IMD 10B, which may include an insulative layer as describedbelow.

FIG. 4A is a conceptual drawing illustrating another example IMD 10Athat may be substantially similar to IMD 10 of FIG. 1 . In addition tothe components illustrated in FIGS. 1-3 , the example of IMD 10illustrated in FIG. 4A also may include a body portion 70 and anattachment plate 72. Attachment plate 72 may be configured tomechanically couple header 32 to body portion 70 of IMD 10A. Bodyportion 70 of IMD 10A may be configured to house one or more of theinternal components of IMD 10 illustrated in FIG. 3 , such as one ormore of processing circuitry 50, sensing circuitry 52, communicationcircuitry 54, memory 56, and/or internal components of sensors 58. Insome examples, body portion 70 may be formed of one or more of titanium,ceramic, or any other suitable biocompatible materials.

FIG. 4B is a conceptual drawing illustrating another example IMD 10Bthat may include components substantially similar to IMD 10 of FIG. 1 .In addition to the components illustrated in FIGS. 1-3 , the example ofIMD 10B illustrated in FIG. 4B also may include a wafer-scale insulativecover 74, which may help insulate electrical signals passing betweenelectrodes 16A, 16B on housing 14B and processing circuitry 50. In someexamples, insulative cover 74 may be positioned over an open housing 14to form the housing for the components of IMD 10B. One or morecomponents of IMD 10B (e.g., antenna 26, processing circuitry 50,sensing circuitry 52, and/or communication circuitry 54 may be formed ona bottom side of insulative cover 74, such as by using flip-chiptechnology. Insulative cover 74 may be flipped onto a housing 14B. Whenflipped and placed onto housing 14B, the components of IMD 10B formed onthe bottom side of insulative cover 74 may be positioned in a gap 78defined by housing 14B. Housing 14B may be formed from titanium or anyother suitable material (e.g., a biocompatible material), and may have athickness of about 200 micrometers to about 500 micrometers. Thesematerials and dimensions are examples only, and other materials andother thicknesses are possible for devices of this disclosure.

FIG. 5 is a functional block diagram illustrating an example system thatincludes an access point 90, a network 92, external computing devices,such as a server 94, and one or more other computing devices 100A-100N,which may be coupled to IMD 10, external device 12, and external device12 via network 92. In this example, IMD 10 may use communicationcircuitry 54 to communicate with external device 12 via a first wirelessconnection, and to communicate with an access point 90 via a secondwireless connection. In the example of FIG. 5 , access point 90,external device 12, server 94, and computing devices 100A-100N areinterconnected and may communicate with each other through network 92.

Access point 90 may comprise a device that connects to network 92 viaany of a variety of connections, such as telephone dial-up, digitalsubscriber line (DSL), or cable modem connections. In other examples,access point 90 may be coupled to network 92 through different forms ofconnections, including wired or wireless connections. In some examples,access point 90 may be a user device, such as a tablet, smartphone, orwearable device (e.g., in the form of a watch, necklace, or otherwearable item), that may be co-located with the patient. Such wearabledevices may include one or more electrodes or other sensors configuredfor sensing signals used in determining values of at least one HBVmetric in accordance with the techniques described herein. As discussedabove, IMD 10 may be configured to transmit data, such as current valuesand heart failure statuses, to external device 12. In addition, accesspoint 90 may interrogate IMD 10, such as periodically or in response toa command from the patient or network 92, in order to retrieve currentvalues or heart failure statuses determined by processing circuitry 50of IMD 10, or other operational or patient data from IMD 10. Accesspoint 90 may then communicate the retrieved data to server 94 vianetwork 92.

In some cases, server 94 may be configured to provide a secure storagesite for data that has been collected from IMD 10, and/or externaldevice 12. In some cases, server 94 may assemble data in web pages orother documents for viewing by trained professionals, such asclinicians, via computing devices 100A-100N. One or more aspects of theillustrated system of FIG. 5 may be implemented with general networktechnology and functionality, which may be similar to that provided bythe Medtronic CareLink® Network developed by Medtronic plc, of Dublin,Ireland.

In some examples, one or more of computing devices 100A-100N (e.g.,device 100A) may be a tablet or other smart device located with aclinician, by which the clinician may program, receive alerts from,and/or interrogate IMD 10. For example, the clinician may accessdetermined values of one or more HBV metrics of patient 4 and/or otherinformation associated with the heart failure status of patient 4through device 100A, such as when patient 4 is in in between clinicianvisits, to check on a heart failure status of patient 4 as desired. Insome examples, the clinician may enter instructions for a medicalintervention for patient 4 into an app in device 100A, such as based ona heart failure status of patient 4 determined by IMD 10 and/or otherpatient data known to the clinician. Device 100A then may transmit theinstructions for medical intervention to another of computing devices100A-100N (e.g., device 100B) located with patient 4 or a caregiver ofpatient 4. For example, such instructions for medical intervention mayinclude an instruction to change a drug dosage, timing, or selection, toschedule a visit with the clinician, or to seek medical attention. Infurther examples, device 100B may generate an alert to patient 4 basedon a health status of patient 4 determined by IMD 10, which may enablepatient 4 proactively to seek medical attention prior to receivinginstructions for a medical intervention. In some examples, the alertgenerated by device 100B may include an updated diagnosis of one or morehealth conditions such as a heart failure condition. For example, thealert may include a diagnosis that a heart failure condition of patient4 has progressed from a first type of heart failure condition to asecond type of heart failure condition. In this manner, patient 4 may beempowered to take action, as needed, to address his or her heart failurestatus, which may help improve clinical outcomes for patient 4.

FIGS. 6-8 are flow diagrams illustrating various techniques related todetermining a heart failure status of a patient based on a comparison ofa difference between HBV values of the patient or a current HBV value ofthe patient to corresponding baseline values and transmittinginstructions for medical intervention to a user interface. As describedherein, the techniques illustrated FIGS. 6-8 may be employed using oneor more components of medical device system 2, which have been describedabove with respect to FIGS. 1-5 . Although described as being performedby processing circuitry 50 of IMD 10 for the sake of clarity, thetechniques of FIGS. 6-8 may be performed, in whole or in part, byprocessing circuitry and memory of other devices of medical devicesystem 2, as described herein. For example, one or more devices (e.g.,external device 12 or other external device or server) or a clinicianmay, in some examples, carry out one or more steps attributed below toprocessing circuitry 50 of IMD 10.

FIG. 6 is a flow diagram illustrating an example technique fordetermining, by processing circuitry of medical device system 2 (e.g.,processing circuitry 50 of IMD 10), a heart failure status of patient 4based on a difference between a first value of at least one HBV metricof patient 4 and a second value of the at least one HBV metric ofpatient 4. The at least one HBV metric may be, for example, at least oneof a T-wave alternans metric (e.g., a beat-to-beat variation in anamplitude or shape of a T-wave of a cardiac cycle), a PR duration metric(e.g., a beat-to-beat variability of a duration of a PR interval of acardiac cycle), a short-term variability metric ((STV); which mayquantify beat-to-beat variability in repolarization), or aphase-rectified signal averaging metric ((PRSA); which may characterizethe capacity of heart 6 to decelerate or accelerate a cardiac rhythm andmay be associated with cardiovascular risk).

According to the example of FIG. 6 , processing circuitry 50 determinesthat an activity state of patient 4 satisfies at least one inactivitycriterion based on at least one first signal received by processingcircuitry 50 from sensors 58, such as from one or more accelerometers,one or more microphones, one or more pressure sensors, and/or one ormore of electrodes 16A, 16B (110). The at least one inactivity criterionmay be, in some examples, at least one of an activity level, posture,heart rate, or respiration rate of patient 4, or a time of day. Inexamples in which the at least one inactivity criterion includes a timeof day, processing circuitry 50 may be configured to account for changesin time that may occur, such as when patient 4 travels from one timezone to another time zone and/or when daylight-savings time beginsand/or ends. In some examples, the at least one inactivity criterion maybe associated with patient 4 lying down and/or being asleep.

After determining that the activity state of patient 4 satisfies the atleast one inactivity criterion, processing circuitry 50 then determinesa first value of the at least one HBV metric of patient 4 while theactivity state of patient 4 satisfies the at least one inactivitycriterion, based on at least one second signal received by processingcircuitry from sensors 58 such as from one or more accelerometers, oneor more microphones, one or more pressure sensors, and/or one or more ofelectrodes 16A, 16B (112). For example, the at least one second signalmay be a cardiac electrogram signal received by processing circuitry 50from electrodes 16A and 16B or any other combination of at least twoelectrodes on IMD 10. In other examples, the at least one second signalmay be received by processing circuitry 50 from one or moreaccelerometers, microphones, and/or pressure sensors and indicative ofheart sounds associated with a heart rate of patient 4, on which basisprocessing circuitry 50 may determine the first value of the at leastone HBV metric of patient 4. In any such examples, processing circuitry50 may determine the first value of the at least one HBV metric ofpatient 4 by collecting a plurality of values of the at least one HBVmetric during a measurement period. In some examples, such a measurementperiod may be a period of time encompassing a plurality of cardiaccycles of patient 4, such as 30 seconds, one minute, or any othersuitable period of time. Processing circuitry 50 then may determine adifference between each value and a subsequent value collected duringthe measurement period and average or otherwise analyze the differencesto determine the first value of the at least one HBV metric of patient4.

After determining the first value of the at least one HBV metric,processing circuitry 50 then determines that the activity state ofpatient 4 no longer satisfies the at least one inactivity criterionbased on the at least one first signal received by processing circuitryfrom sensors 58 (114). In examples in which the at least one inactivitycriterion is associated with patient 4 lying down and/or being asleep,the determination that the activity state of patient 4 no longersatisfies the at least one inactivity criterion may comprise adetermination that patient 4 is in an upright posture and/or hasawakened from a sleep state. Processing circuitry 50 then determines,within a predetermined period of time after determining that theactivity state of patient 4 no longer satisfies the at least oneinactivity criterion, a second value of the at least one HBV metricwhile the activity state of patient 4 no longer satisfies the at leastone inactivity criterion based on the at least one second signal (116).Processing circuitry 50 may determine the second value of the at leastone HBV metric of patient 4 by collecting a plurality of values of theat least one HBV metric during a measurement period within thepredetermined period of time. In some examples, such a measurementperiod may be a period of time encompassing a plurality of cardiaccycles of patient 4, such as 30 seconds, one minute, or any othersuitable period of time. Processing 50 then may determine a differencebetween each value and a subsequent value collected during themeasurement period and average or otherwise analyze the differences todetermine the second value of the at least one HBV metric of patient 4.

Determining the first value of the at least one HBV metric while theactivity state of patient 4 satisfies the at least one inactivitycriterion and determining the second value of the at least one HBVmetric while the activity state of patient 4 no longer satisfies the atleast one inactivity criterion may enable processing circuitry 50 toidentify changes in vascular tone of patient 4 that occur within thepredetermined period of time after the activity state of patient 4ceases to satisfy the at least one inactivity criterion. Changes invascular tone of patient 4 during the predetermined period of time(e.g., about 30 minutes), which processing circuitry 50 may identifybased on changes in values of the at least one HBV metric, may reflectchanges in the balance between sympathetic activity and vagal tone ofpatient 4. As discussed above, changes in the balance betweensympathetic activity and vagal tone of patient 4, occurring eitherduring one activity cycle or across multiple activity cycles, may beassociated with changes in a heart failure condition of patient 4. Thus,determining the second value of the at least one HBV metric during thepredetermined period of time may help enable processing circuitry 50 todetermine the heart failure status of patient 4.

In some examples, prior to determining that the activity state ofpatient 4 no longer satisfies the at least one inactivity criterion,processing circuitry 50 may determine that a period of time during whichthe activity state of patient 4 is expected to satisfy the at least oneinactivity criterion, such as a time during which patient 4 is expectedto be asleep has elapsed. Based on the determination that the period oftime during which the activity state of patient 4 is expected to satisfythe at least one activity criterion has elapsed, processing circuitry 50may increase a frequency at which an activity state and/or a value ofthe at least one HBV metric of patient 4 are determined in order toidentify or approximate the time at which the activity state of patient4 ceases to satisfy the at least one inactivity criterion. Identifyingor approximating the time at which the activity state of patient 4ceases to satisfy the at least one inactivity criterion may enableprocessing circuitry 50 to identify or approximate the beginning of thepredetermined period of time and determine the second value of the atleast one HBV metric within the predetermined period of time.

After determining the first and second values of the at least one HBVmetric of patient 4, processing circuitry 50 determines a differencebetween the first and second values of the at least one HBV metric (118)and determines a heart failure status of patient 4 based on thedifference (120). Processing circuitry 50 may determine the heartfailure status of patient 4 by determining whether the differencebetween the first and second values of the at least one HBV metricsatisfies an HBV difference threshold value associated with a change inthe heart failure status of patient 4, which may be stored inbaseline/threshold tables 64 of memory 56.

In some examples, processing circuitry 50 may periodically determine anupdated value of the HBV difference threshold, which may enableprocessing circuitry 50 to track trends in the difference between thefirst and second values of the at least one HBV metric over multipleactivity cycles of patient 4. The difference between the first andsecond values of the at least one HBV metric may correspond to a currentactivity cycle of patient 4. An activity cycle may include a period oftime in which processing circuitry 50 both determines that the activitystate of patient 4 satisfies the at least one inactivity criterion anddetermines that the activity state of patient 4 no longer satisfies theat least one inactivity criterion. Thus, in such examples, processingcircuitry 50 may determine the HBV difference threshold value based onone or more values of the difference between the first and second valuesof the at least one HBV metric that correspond to one or more previousactivity cycles of patient 4.

In some examples, an HBV difference threshold value may be an absolutevalue of a percentage of a baseline value of the difference between thefirst and second values of the at least one HBV metric, which may be abaseline value specific to patient 4. For example, if a baseline valueof the difference between the first and second values of the at leastone HBV metric=X, then an HBV difference threshold value may be X±0.2X.In other examples, the difference between the first and second values ofthe at least one HBV metric may be associated with multiple HBVdifference threshold values that correspond to different percentages ofthe baseline value, thereby taking into account differences insignificance between values that exceed a baseline value and values thatare less than a baseline value. For example, if a baseline value of thedifference between the first and second values of the at least one HBVmetric=X, then HBV difference threshold values of the difference betweenthe first and second values of the at least one HBV metric may be X+0.2Xand X−0.1X. In such an example, values of the difference between thefirst and second values of the at least one HBV metric that are lessthan X, which may be associated with an already-advanced stage of aheart failure condition of patient 4, have relatively greatersignificance than values of that are greater than X, although therelative significance of difference values may be determined based onthe individual patient. In any such examples, the threshold values maybe based on deviations from corresponding baseline values, such asstandard deviations or any other suitable statistical functions.

Processing circuitry 50 may repeat steps 110-120 to periodicallydetermine updated heart failure statuses of patient 4 such as daily,weekly, monthly, or at any other suitable period. In some examples, theheart failure status of patient 4 may indicate a possibility thatpatient 4 may experience an adverse medical event within a certainperiod of time, such as a recurrence of symptoms, acute heart failuredecompensation, or other adverse medical events that may require medicalintervention such as hospitalization. Processing circuitry thentransmits the health status of patient 4 to a remote computer, such asexternal device 12 (122). In some examples, processing circuitry 50 maytransmit the heart failure status of patient 4 to the remote computereach time processing circuitry 50 determines the heart failure status ofpatient 4. In other examples, processing circuitry 50 may transmit theheart failure status of patient 4 to the remote computer lessfrequently, such as weekly or at any other suitable interval.

FIG. 7 is a flow diagram illustrating another example technique fordetermining, by processing circuitry of medical device system 2 (e.g.,processing circuitry 50), a heart failure status of patient 4 based on acomparison of a value of at least one HBV metric of patient 4 to abaseline value of the at least one HBV metric, which may be stored inbaseline/threshold tables 64 of memory 56. One or more aspects of theexample technique illustrated in FIG. 7 may be substantially similar toone or more aspects of the example technique illustrated in FIG. 6 . Theexample technique of FIG. 7 may differ from the example technique ofFIG. 6 in that processing 50 may determine the heart failure status ofpatient 4 based on a difference between the current value of the atleast one HBV metric, which may be determined while an activity state ofpatient 4 does not satisfy at least one inactivity criterion, and thebaseline value of the at least one HBV metric, which also may beassociated with a baseline activity state of patient 4 that does notsatisfy the at least one inactivity criterion. Thus, the technique ofFIG. 7 may be used, for example, to monitor changes in an absolute valueof the at least one HBV metric occurring during a particular portion ofan activity cycle of patient 4 over the course of multiple activitycycles. In contrast, in the example technique of FIG. 6 , processingcircuitry may determine the heart failure status of patient 4 based onthe difference between first and second determined values of at leastone HBV metric, which processing 50 respectively may determine when theactivity state of patient 4 satisfies and no longer satisfies at leastone inactivity criterion. Thus, the technique of FIG. 6 may be used, forexample, to monitor changes in a difference between values of at leastone HBV metric that occur during different portions of an activity cycleof patient 4 over the course of multiple activity cycles. In someinstances, the example techniques of FIGS. 6 and 7 may be used tomonitor different aspects of a heart failure status of patient 4.

According to the example of FIG. 7 , processing circuitry 50 determinesthat an activity state of patient 4 satisfies at least one inactivitycriterion based on at least one first signal received by processingcircuitry 50 from sensors 58, such as from one or more accelerometers,one or more microphones, one or more pressure sensors, and/or one ormore of electrodes 16A, 16B (130), such as in a manner substantiallysimilar to that described above with respect to (110) of FIG. 6 . Afterdetermining that the activity state of patient 4 satisfies the at leastone inactivity criterion, processing 50 then determines that theactivity state of patient 4 has increased by determining that theactivity state of patient 4 no longer satisfies the at least oneinactivity criterion based on the at least one first signal received byprocessing circuitry 50 from sensors 58 (132), such as in a mannersubstantially similar to that described above with respect to (114) ofFIG. 6 . In some examples in which the at least one inactivity criterionis associated with patient 4 lying down and/or being asleep, thedetermination that the activity state of patient 4 has increased maycomprise a determination that patient 4 is in an upright posture and/orhas awakened from a sleep state.

Processing circuitry 50 then determines, within a predetermined periodof time after determining that the activity state of patient 4 hasincreased, a current value of the at least one HBV metric of patient 4while the activity state of patient 4 no longer satisfies the at leastone inactivity criterion based on the at least one second signalreceived from sensors 58 by processing circuitry 50 (134). In someexamples, processing circuitry 50 may determine the current value of theat least one HBV metric of patient 4 in a manner substantially similarto the manner in which processing circuitry 50 may determine the secondvalue of the at least one HBV metric of patient 4 as described abovewith respect to (116) of FIG. 6 .

After determining the current value of the at least one HBV metric ofpatient 4, processing circuitry 50 compares the current value of the atleast one HBV metric to the baseline value of the at least one HBVmetric (136) and determines a heart failure status of patient 4 based onthe difference (138). Processing circuitry 50 may determine the heartfailure status of patient 4 by determining whether a difference betweenthe current and baseline values of the at least one HBV metric satisfiesan HBV threshold value associated with a change in the heart failurestatus of patient 4, which may be stored in baseline/threshold tables 64of memory 56.

In some examples, processing circuitry 50 may periodically determine anupdated value of the HBV threshold, such as in a manner similar to themanner in which processing circuitry 50 may determine updated values ofthe HBV difference threshold values described above with respect to FIG.6 , such as based on current values of the at least one HBV metric thatcorrespond to one or more previous activity cycles of patient 4.Periodically determining updated values of the HBV threshold may enableprocessing circuitry 50 to track trends in the current value of the atleast one HBV metric of patient 4 that occur during the same portion ofan activity cycle of patient 4 (e.g., within about 30 minutes of anactivity state of patient 4 increasing, such as upon awakening from asleep state) over the course of multiple activity cycles.

As with steps 110-120 of the example of FIG. 6 , processing circuitry 50may repeat steps 130-138 to periodically determine updated heart failurestatuses of patient 4 such as daily, weekly, monthly, or at any othersuitable period. In some examples, the heart failure status of patient 4may indicate a possibility that patient 4 may experience an adversemedical event within a certain period of time, such as a recurrence ofsymptoms, acute heart failure decompensation, or other adverse medicalevents that may require medical intervention such as hospitalization.Processing circuitry then transmits the health status of patient 4 to aremote computer, such as external device 12 (140). In some examples,processing circuitry 50 may transmit the heart failure status of patient4 to the remote computer each time processing circuitry 50 determinesthe heart failure status of patient 4. In other examples, processingcircuitry 50 may transmit the heart failure status of patient 4 to theremote computer less frequently, such as weekly or at any other suitableinterval.

FIG. 8 is a flow diagram illustrating an example technique for a remotecomputer (e.g., external device 12) to determine instructions for amedical intervention based on a heart failure status of patient 4received from IMD 10 and transmit the instructions to a user interface.The method illustrated in FIG. 8 may be used with any of the methods fordetermining a health status by IMD 10 described herein, such as themethods illustrated in FIGS. 6 and 7 . In some examples, external device12 is configured to receive a heart failure status of patient 4 from IMD10, which may be transmitted to a processing circuitry of externaldevice 12 via communication circuitry 54 and antenna 26 of IMD 10 (150).In some examples, the heart failure status of patient 4 may include apossibility that the patient will experience an adverse medical event,such as recurrent symptom(s), acute decompensation, hospitalization, orother adverse medical events.

In some examples, upon receiving the heart failure status of patient 4from IMD 10 and prior to determining instructions for a medicalintervention for patient 4, external device 12 may transmit one or morequeries to a user device. For example, external device 12 may askpatient 4 or a caregiver to answer questions about recent or currentactivities or symptoms of patient 4, such as whether patient 4 recentlyhas exercised, taken medications, or experienced symptoms. In addition,external device 12 may interrogate IMD 10 for recently-determined orcurrent values of at least one HBV metric of patient 4, differencesbetween first and second values of at least one HBV metric of patient 4,differences between current and baseline values of at least one HBV ofpatient 4, and/or heart-failure status determinations pertaining topatient 4, if IMD 10 did not already transmit such values, differences,and/or heart-failure status determinations to external device 12. Basedon the heart failure status of patient 4, and optionally based onanswers to queries and/or the current values of patient 4, externaldevice 12 then may determine instructions for a medical intervention forpatient 4 (152).

External device 12 may determine instructions for one or more medicalinterventions for patient 4 based on the heart failure status of patient4. For example, external device 12 may determine instructions formodifying (e.g., start, stop, increase, or decrease) a dose of one ormore drugs, such as diuretics, nitrates, beta-blockers, ivabradine, orinotropes. In some examples, instructions for medical interventions forpatient 4 may take into account the presence of cardiac arrhythmia, asindicated by ECG signals of patient 4 detected by IMD 10. For example,instructions determined by external device 12 in the presence ofarrhythmia may include instructions for patient 4 to avoid takingcertain medications, instruct patient 4 to visit a healthcare facility,or may recommend starting CRT or changing CRT parameters.

In some examples, external device 12 may determine the instructions formedical intervention independent of clinician input, such as byselecting among treatment options stored in a memory of external device12 or a centralized database that are associated withrecently-determined or current values of at least one HBV metric ofpatient 4, differences between first and second values of at least oneHBV metric of patient 4, differences between current and baseline valuesof at least one HBV of patient 4, and/or heart-failure statusdeterminations pertaining to patient 4. In other examples, a clinicianmay determine the instructions for medical intervention on substantiallythe same basis, and input the instructions to external device 12.External device 12 then may transmit the instructions to an interface ofthe user device with patient 4 (154). In some examples, external device12 may transmit follow-up queries to patient 4 or a caregiver via theuser device after transmitting the instructions. Such queries mayinclude questions pertaining to patient 4's understanding of thetransmitted instructions, whether patient 4 has complied with theinstructed medical intervention, and/or whether patient 4 isexperiencing symptoms. External device 12 may store patient 4'sresponses in a memory of external device 12, or in a centralizeddatabase. A clinician may review the responses, and remotely follow-upwith patient 4 as needed following any changes to patient 4's treatmentfor a heart failure condition. In this manner, the techniques andsystems described herein advantageously may enable patient 4 to receiveindividualized, frequently updated treatment at less expense than acomparable number of clinician visits and/or hospitalizations wouldincur. In addition, the techniques and systems may help reducerecurrence of symptoms, acute decompensation events, and/or cardiacremodeling that may be caused by acute decompensation episodes, which inturn may help reduce or slow the progression of a heart failurecondition of patient 4.

Although the example techniques for determining a heart failure statusof a patient are described herein as being based on the parameter ofHBV, such examples are not intended to be limiting. In some examples, atechnique for determining a heart failure status of a patient may bebased on one or more other parameters indicative of the heart failurestatus of the patient in combination with HBV. Examples of such otherparameters, and techniques for determining heart failure status based ona plurality of parameters, are described in U.S. Patent ApplicationPublication No. 2012/0253207 by Sarkar et al. and “Development andvalidation of an integrated diagnostic algorithm derived from parametersmonitored in implantable devices for identifying patients at risk forheart failure hospitalization in an ambulatory setting” by Cowie et al.,which are incorporated by reference herein in their entirety. Stillother example parameters that may be used in such a technique fordetermining a heart failure status of a patient may include parameterssuch as edema (e.g., peripheral edema), pulse transit time, and/or othersuitable parameters.

Various aspects of the techniques may be implemented within one or moreprocessors, including one or more microprocessors, DSPs, ASICs, FPGAs,or any other equivalent integrated or discrete logic circuitry, as wellas any combinations of such components, embodied in programmers, such asphysician or patient programmers, electrical stimulators, or otherdevices. The term “processor” or “processing circuitry” may generallyrefer to any of the foregoing logic circuitry, alone or in combinationwith other logic circuitry or any other equivalent circuitry.

In one or more examples, the functions described in this disclosure maybe implemented in hardware, software, firmware, or any combinationthereof. If implemented in software, the functions may be stored on, asone or more instructions or code, a computer-readable medium andexecuted by a hardware-based processing unit. Computer-readable mediamay include computer-readable storage media forming a tangible,non-transitory medium. Instructions may be executed by one or moreprocessors, such as one or more DSPs, ASICs, FPGAs, general purposemicroprocessors, or other equivalent integrated or discrete logiccircuitry. Accordingly, the terms “processor” or “processing circuitry”as used herein may refer to one or more of any of the foregoingstructures or any other structure suitable for implementation of thetechniques described herein.

In addition, in some aspects, the functionality described herein may beprovided within dedicated hardware and/or software modules. Depiction ofdifferent features as modules or units is intended to highlightdifferent functional aspects and does not necessarily imply that suchmodules or units must be realized by separate hardware or softwarecomponents. Rather, functionality associated with one or more modules orunits may be performed by separate hardware or software components, orintegrated within common or separate hardware or software components.Also, the techniques could be fully implemented in one or more circuitsor logic elements. The techniques of this disclosure may be implementedin a wide variety of devices or apparatuses, including an IMD, anexternal programmer, a combination of an IMD and external programmer, anintegrated circuit (IC) or a set of ICs, and/or discrete electricalcircuitry, residing in an IMD and/or external programmer.

The following examples are illustrative of the techniques describedherein.

Example 1: A method for determining a heart failure status of a patientusing a medical device comprising one or more sensors, the methodcomprising, by processing circuitry of a medical device systemcomprising the medical device: determining that an activity state of thepatient satisfies at least one inactivity criterion based on at leastone first signal received from the one or more sensors; determining afirst value of at least one heart beat variability metric of the patientwhile the activity state of the patient satisfies the at least oneinactivity criterion based on at least one second signal received fromthe one or more sensors; determining, after determining the first valueof the at least one heart beat variability metric, that the activitystate of the patient no longer satisfies the at least one inactivitycriterion based on the at least one first signal; determining, within apredetermined period of time after determining that the activity stateof the patient no longer satisfies the at least one inactivitycriterion, a second value of the at least one heart beat variabilitymetric while the activity state of the patient no longer satisfies theat least one inactivity criterion based on the at least one secondsignal; determining a difference between the first value of the at leastone heart beat variability metric and the second value of the at leastone heart beat variability metric; and determining the heart failurestatus of the patient based on the difference.

Example 2: The method of example 1, wherein determining the heartfailure status of the patient based on the difference between the firstvalue of the at least one heart beat variability metric and the secondvalue of the at least one heart beat variability metric beat variabilitycomprises determining whether the difference between the first value ofthe at least one heart beat variability metric and the second value ofthe at least one heart beat variability metric satisfies a heart beatvariability difference threshold value that is associated with a changein the heart failure status of the patient.

Example 3: The method of example 2, wherein the difference between thefirst value of the at least one heart beat variability metric and thesecond value of the at least one heart beat variability metriccorresponds to a current activity cycle of the patient, an activitycycle comprising a period of time in which the processing circuitry bothdetermines that the activity state of the patient satisfies the at leastone inactivity criterion and determines that the activity state of thepatient no longer satisfies the at least one inactivity criterion,wherein the heart beat variability difference threshold value is basedon one or more values of the difference between the first value of theat least one heart beat variability metric and the second value of theat least one heart beat variability metric that correspond to one ormore previous activity cycles of the patient.

Example 4: The method of example 2, wherein the at least one secondsignal is a current at least one second signal, the first value of theat least one heart beat variability metric is a current first value ofthe at least one heart beat variability metric, and the second value ofthe at least one heart beat variability metric is a current second valueof the at least one heart beat variability metric, the method furthercomprising: receiving at least one baseline second signal from at leasttwo of the plurality of electrodes that are the same as or differentfrom the one or more sensors from which the processing circuitryreceives the current at least one second signal; determining a baselinefirst value of the at least one heart beat variability metric and abaseline second value of the at least one heart beat variability metricbased on the at least one baseline second signal; determining a baselinedifference between the baseline first value of the at least one heartbeat variability metric and the baseline second value of the at leastone heart beat variability metric; and determining the heart beatvariability difference threshold value based on the baseline differencebetween the baseline first value of the at least one heart beatvariability metric and the baseline second value of the at least oneheart beat variability metric.

Example 5: The method of any of examples 1 to 4, further comprising, bythe processing circuitry: transmitting the heart failure status of thepatient to a remote computer; receiving, from the remote computer,instructions for a medical intervention based on the heart failurestatus of the patient; and transmitting the instructions for the medicalintervention to a user interface.

Example 6: The method of example 5, wherein the instructions for themedical intervention comprise at least one of a change in a drugselection, a change in a drug dosage, instructions to schedule a visitwith a clinician, or instructions for the patient to seek medicalattention.

Example 7: The method of any of examples 1 to 6, wherein determining theheart failure status of the patient based on the comparison comprisesdetermining, by the processing circuitry, a possibility that the patientwill experience an adverse medical event.

Example 8: The method of any of examples 1 to 7, wherein the at leastone inactivity criterion comprises a value of least one of a patientactivity level, a patient posture, a time of day, a patient heart rate,or a patient respiration rate.

Example 9: The method of any of examples 1 to 8, further comprisingdetermining, prior to determining that the activity state of the patientno longer satisfies the at least one inactivity criterion, that a periodof time during which the activity state of the patient is expected tosatisfy the at least one inactivity criterion has elapsed.

Example 10: The method of any of examples 1 to 9, wherein determiningthat the activity state of the patient satisfies the at least oneinactivity criterion comprises determining that the patient is lyingdown based on the at least one first signal, and wherein determining thefirst value of the at least one heart beat variability metric while theactivity state of the patient satisfies the at least one inactivitycriterion comprises determining the first value of the at least oneheart beat variability metric while the patient is lying down.

Example 11: The method of example 10, wherein determining that theactivity state of the patient no longer satisfies the at least oneinactivity criterion comprises determining that the patient is in anupright posture based on the at least one first signal, and whereindetermining the second value of the at least one heart beat variabilitymetric while the activity level of the patient no longer satisfies theat least one inactivity criterion comprises determining the second valueof the at least one heart beat variability metric while the patient isin the upright posture.

Example 12: The method of example 11, wherein determining that thepatient is in the upright posture while the activity state of thepatient no longer satisfies the at least one inactivity criterion afterdetermining the first value of the at least one heart beat variabilitymetric comprises determining that the patient has awakened from a sleepstate, and wherein the predetermined period of time comprises apredetermined period of time after the patient has awakened from thesleep state.

Example 13: The method of example 12, wherein the predetermined periodof time is a period of time occurring within about 30 minutes after thepatient has awakened from the sleep state.

Example 14: The method of any of examples 1 to 13, wherein the firstvalue of the at least one heart beat variability metric comprises avalue of at least one sleeping heart beat variability metric and thesecond value of the at least one heart beat variability metric comprisesa value of at least one waking heart beat variability metric, whereindetermining that the activity state of the patient no longer satisfiesthe at least one inactivity criterion after determining the first valueof the at least one heart beat variability metric comprises determiningthat the patient has awakened from a sleep state, and wherein thepredetermined period of time comprises a predetermined period of timeafter the patient has awakened from the sleep state.

Example 15: The method of example 14, wherein the predetermined periodof time is a period of time occurring within about 30 minutes after thepatient has awakened from the sleep state.

Example 16: The method of any of examples 1 to 15, wherein determiningthe first value of the at least one heart beat variability metriccomprises determining a representative first value of the at least oneheart beat variability metric based on a plurality of first values ofthe at least one heart beat variability metrics determined while theactivity state of the patient satisfies the at least one inactivitycriterion.

Example 17: The method of any of examples 1 to 16, wherein the at leastone heart beat variability metric comprises two or more heart beatvariability metrics, wherein determining the first value of the at leastone heart beat variability metric comprises determining a first value ofeach of the at least two heart beat variability metrics, and whereindetermining the second value of the at least one heart beat variabilitymetric comprises determining a second value of each of the at least twoheart beat variability metrics.

Example 18: The method of any of examples 1 to 17, wherein the at leastone heart beat variability metric comprises a corresponding at least oneof a T-wave alternans metric, a PR duration metric, a short-termvariability metric, or a phase-rectified signal averaging metric.

Example 19: The method of any of examples 1 to 18, wherein the at leastone heart beat variability metric comprises at least one heart ratevariability metric.

Example 20: The method of any of examples 1 to 19, wherein the firstsignal and the second signal comprise a common signal.

Example 21: The method of any of examples 1 to 20, wherein determiningthe second value of the at least one heart beat variability metriccomprises determining a representative second value of the at least oneheart beat variability metric based on a plurality of second values ofthe at least one heart beat variability metric determined during thepredetermined period of time.

Example 22: The method of any of examples 1 to 21, further comprisingdetermining an arrhythmia-prone status of the patient based on thedifference.

Example 23: The method of any of examples 1 to 22, wherein the one ormore sensors comprise a plurality of electrodes, and wherein the atleast one second signal comprises a cardiac electrogram signal receivedfrom at least two of the plurality of electrodes.

Example 24: The method of any of examples 1 to 23, wherein the medicaldevice is an implantable medical device configured for implantationwithin the patient.

Example 25: The method of example 24, wherein the implantable medicaldevice comprises a housing configured for subcutaneous implantation, andwherein the one or more sensors are positioned on or within the housing.

Example 26: The method of example 24, wherein the implantable medicaldevice comprises a leadless implantable medical device.

Example 27: A system for determining a heart failure status of a patientusing a medical device, the system comprising: the medical device,wherein the medical device comprises one or more sensors; and processingcircuitry. The processing circuitry is configured to: determine that anactivity state of the patient satisfies at least one inactivitycriterion based on at least one first signal received from the one ormore sensors; determine a first value of at least one heart beatvariability metric of the patient while the activity state of thepatient satisfies the at least one inactivity criterion based on atleast one second signal received from the one or more sensors;determine, after determining the first value of the at least one heartbeat variability metric, that the activity state of the patient nolonger satisfies the at least one inactivity criterion based on the atleast one first signal; determine, within a predetermined period of timeafter determining that the activity state of the patient no longersatisfies the at least one inactivity criterion, a second value of theat least one heart beat variability metric while the activity state ofthe patient no longer satisfies the at least one inactivity criterionbased on the at least one second signal; determine a difference betweenthe first value of the at least one heart beat variability metric andthe second value of the at least one heart beat variability metric; anddetermine the heart failure status of the patient based on thedifference.

Example 28: The system of example 27, wherein the processing circuitryis configured to determine the heart failure status of the patient basedon the difference between the first value of the at least one heart beatvariability metric and the second value of the at least one heart beatvariability metric to the baseline heart beat variability difference byat least determining whether the difference between the first value ofthe at least one heart beat variability metric and the second value ofthe at least one heart beat variability metric satisfies a heart beatvariability difference threshold value that is associated with a changein the heart failure status of the patient.

Example 29: The system of example 28, wherein the difference between thefirst value of the at least one heart beat variability metric and thesecond value of the at least one heart beat variability metriccorresponds to a current activity cycle of the patient, an activitycycle comprising a period of time in which the processing circuitry bothdetermines that the activity state of the patient satisfies the at leastone inactivity criterion and determines that the activity state of thepatient no longer satisfies the at least one inactivity criterion,wherein the heart beat variability difference threshold value is basedon one or more values of the difference between the first value of theat least one heart beat variability metric and the second value of theat least one heart beat variability metric that correspond to one ormore previous activity cycles of the patient.

Example 30: The system of example 28, wherein the at least one secondsignal is a current at least one second signal, the first value of theat least one heart beat variability metric is a current first value ofthe at least one heart beat variability metric, and the second value ofthe at least one heart beat variability metric is a current second valueof the at least one heart beat variability metric, wherein theprocessing circuitry is further configured to: receive at least onebaseline second signal from at least two of the plurality of electrodesthat are the same as or different from the one or more sensors fromwhich the processing circuitry receives the current at least one secondsignal; determine a baseline first value of the at least one heart beatvariability metric and a baseline second value of the at least one heartbeat variability metric based on the at least one baseline secondsignal; determine a baseline difference between the baseline first valueof the at least one heart beat variability metric and the baselinesecond value of the at least one heart beat variability metric; anddetermine the heart beat variability difference threshold value based onthe difference between the baseline first value of the at least oneheart beat variability metric and the baseline second value of the atleast one heart beat variability metric.

Example 31: The system of any of examples 27 to 30, wherein theprocessing circuitry is further configured to: transmit the heartfailure status of the patient to a remote computer; receive, from theremote computer, instructions for a medical intervention based on theheart failure status of the patient; and transmit the instructions forthe medical intervention to a user interface.

Example 32: The system of example 31, wherein the instructions for themedical intervention comprise at least one of a change in a drugselection, a change in a drug dosage, instructions to schedule a visitwith a clinician, or instructions for the patient to seek medicalattention.

Example 33: The system of any of examples 27 to 32, wherein theprocessing circuitry is configured to determine the heart failure statusof the patient based on the comparison by at least determining apossibility that the patient will experience an adverse medical event.

Example 34: The system of any of examples 27 to 33, wherein the at leastone inactivity criterion comprises a value of least one of a patientactivity level, a patient posture, a time of day, a patient heart rate,or a patient respiration rate.

Example 35: The system of any of examples 27 to 34, further comprisingdetermining, prior to determining that the activity state of the patientno longer satisfies the at least one inactivity criterion, that a periodof time during which the activity state of the patient is expected tosatisfy the at least one inactivity criterion has elapsed.

Example 36: The system of any of examples 27 to 35, wherein theprocessing circuitry is configured to determine that the activity stateof the patient satisfies the at least one inactivity criterion by atleast determining that the patient is lying down based on the at leastone first signal, and wherein the processing circuitry is configured todetermine the first value of the at least one heart beat variabilitymetric while the activity state of the patient satisfies the at leastone inactivity criterion by at least determining the first value of theat least one heart beat variability metric while the patient is lyingdown.

Example 37: The system of example 36, wherein the processing circuitryis configured to determine that the activity state of the patient nolonger satisfies the at least one inactivity criterion by at leastdetermining that the patient is in an upright posture based on the atleast one first signal, and wherein the processing circuitry isconfigured to determine the second value of the at least one heart beatvariability metric while the activity level of the patient no longersatisfies the at least one inactivity criterion by at least determiningthe second value of the at least one heart beat variability metric whilethe patient is in the upright posture.

Example 38: The system of example 37, wherein the processing circuitryis configured to determine that the patient is in the upright posturewhile the activity state of the patient no longer satisfies the at leastone inactivity criterion after determining the first value of the atleast one heart beat variability metric by at least determining that thepatient has awakened from a sleep state, and wherein the predeterminedperiod of time comprises a predetermined period of time after thepatient has awakened from the sleep state.

Example 39: The system of example 38, wherein the predetermined periodof time is a period of time occurring within about 30 minutes after thepatient has awakened from the sleep state.

Example 40: The system of any of examples 27 to 39, wherein the firstvalue of the at least one heart beat variability metric comprises avalue of at least one sleeping heart beat variability metric and thesecond value of the at least one heart beat variability metric comprisesa value of at least one waking heart beat variability metric, whereinthe processing circuitry is configured to determine that the activitystate of the patient no longer satisfies the at least one inactivitycriterion after determining the first value of the at least one heartbeat variability metric comprises determining that the patient hasawakened from a sleep state, and wherein the predetermined period oftime comprises a predetermined period of time after the patient hasawakened from the sleep state.

Example 41: The system of example 40, wherein the predetermined periodof time is a period of time occurring within about 30 minutes after thepatient has awakened from the sleep state.

Example 42: The system of any of examples 27 to 41, wherein theprocessing circuitry is configured to determine the first value of theat least one heart beat variability metric by at least determining arepresentative first value of the at least one heart beat variabilitymetric based on a plurality of first values of the at least one heartbeat variability metrics determined while the activity state of thepatient satisfies the at least one inactivity criterion.

Example 43: The system of any of examples 27 to 42, wherein the at leastone heart beat variability metric comprises two or more heart beatvariability metrics, wherein the processing circuitry is configured todetermine the first value of the at least one heart beat variabilitymetric by at least determining a first value of each of the at least twoheart beat variability metrics, and wherein the processing circuitry isconfigured to determine the second value of the at least one heart beatvariability metric by at least determining a second value of each of theat least two heart beat variability metrics.

Example 44: The system of any of examples 27 to 43, wherein the at leastone heart beat variability metric comprises a corresponding at least oneof a T-wave alternans metric, a PR duration metric, a short-termvariability metric, or a phase-rectified signal averaging metric.

Example 45: The system of any of examples 27 to 44, wherein the at leastone heart beat variability metric comprises at least one heart ratevariability metric.

Example 46: The system of any of examples 27 to 45, wherein the firstsignal and the second signal comprise a common signal.

Example 47: The system of any of examples 27 to 46, wherein theprocessing circuitry is configured to determine the second value of theat least one heart beat variability metric by at least determining arepresentative second value of the at least one heart beat variabilitymetric based on a plurality of second values of the at least one heartbeat variability metric determined during the predetermined period oftime.

Example 48: The system of any of examples 27 to 47, wherein theprocessing circuitry is further configured to determine anarrhythmia-prone status of the patient based on the difference.

Example 49: The system of any examples 27 to 48, wherein the one or moresensors comprise a plurality of electrodes, and wherein the at least onesecond signal comprises a cardiac electrogram signal received from atleast two of the plurality of electrodes.

Example 50: The system of any of examples 27 to 49, wherein the medicaldevice is an implantable medical device configured for implantationwithin the patient.

Example 51: The system of example 50, wherein the implantable medicaldevice comprises a housing configured for subcutaneous implantation, andwherein the one or more sensors are positioned on or within the housing.

Example 52: The system of example 50, wherein the implantable medicaldevice comprises a leadless implantable medical device.

Example 53: A non-transitory computer-readable medium storinginstructions for causing processing circuitry to perform a method fordetermining a heart failure status of a patient using a medical devicecomprising one or more sensors, the method comprising, by processingcircuitry of a medical device system comprising the medical device:determining that an activity state of the patient satisfies at least oneinactivity criterion based on at least one first signal received fromthe one or more sensors; determining a first value of at least one heartbeat variability metric of the patient while the activity state of thepatient satisfies the at least one inactivity criterion based on atleast one second signal received from the one or more sensors;determining, after determining the first value of the at least one heartbeat variability metric, that the activity state of the patient nolonger satisfies the at least one inactivity criterion based on the atleast one first signal; determining, within a predetermined period oftime after determining that the activity state of the patient no longersatisfies the at least one inactivity criterion, a second value of theat least one heart beat variability metric while the activity state ofthe patient no longer satisfies the at least one inactivity criterionbased on the at least one second signal; determining a differencebetween the first value of the at least one heart beat variabilitymetric and the second value of the at least one heart beat variabilitymetric; and determining the heart failure status of the patient based onthe difference.

Example 54: The non-transitory computer-readable medium of example 53,wherein determining the heart failure status of the patient based on thedifference between the first value of the at least one heart beatvariability metric and the second value of the at least one heart beatvariability metric to the baseline heart beat variability differencecomprises determining whether the difference between the first value ofthe at least one heart beat variability metric and the second value ofthe at least one heart beat variability metric satisfies a heart beatvariability difference threshold value that is associated with a changein the heart failure status of the patient.

Example 55: The non-transitory computer-readable medium of example 54,wherein the difference between the first value of the at least one heartbeat variability metric and the second value of the at least one heartbeat variability metric corresponds to a current activity cycle of thepatient, an activity cycle comprising a period of time in which theprocessing circuitry both determines that the activity state of thepatient satisfies the at least one inactivity criterion and determinesthat the activity state of the patient no longer satisfies the at leastone inactivity criterion, wherein the heart beat variability differencethreshold value is based on one or more values of the difference betweenthe first value of the at least one heart beat variability metric andthe second value of the at least one heart beat variability metric thatcorrespond to one or more previous activity cycles of the patient.

Example 56: A method for determining a heart failure status of a patientusing a medical device comprising one or more sensors, the methodcomprising, by processing circuitry of a medical device systemcomprising the medical device: determining that an activity state of thepatient satisfies at least one inactivity criterion based on at leastone first signal received from the one or more sensors; determining thatthe activity state of the patient has increased by determining that theactivity state of the patient no longer satisfies the at least oneinactivity criterion based on the at least one first signal afterdetermining that the activity state of the patient satisfies the atleast one inactivity criterion; determining, within a predeterminedperiod of time after determining that the activity state of the patienthas increased, a current value of at least one heart beat variabilitymetric while the activity state of the patient no longer satisfies theat least one the inactivity criterion based on at least one secondsignal received from the one or more sensors; comparing the currentvalue of the at least one heart beat variability metric to a baselinevalue of the at least one heart beat variability metric; and determiningthe heart failure status of the patient based on the comparison.

Example 57: The method of example 56, wherein comparing the currentvalue of the at least one heart beat variability metric to the baselinevalue of the at least one heart beat variability metric comprisesdetermining whether a difference between the current value of the atleast one heart beat variability metric and the baseline value of the atleast one heart beat variability metric satisfies at least onecorresponding heart beat variability threshold value that is associatedwith a change in the heart failure status of the patient.

Example 58: The method of example 57, wherein the predetermined periodof time is a current predetermined period of time, and wherein the atleast one corresponding heart beat variability threshold value is basedon one or more corresponding previous values of the at least one heartbeat variability metric that correspond to one or more previouspredetermined periods of time.

Example 59: The method of any of examples 56 to 58, further comprising,by the processing circuitry: transmitting the heart failure status ofthe patient to a remote computer; receiving, from the remote computer,instructions for a medical intervention based on the heart failurestatus of the patient; and transmitting the instructions for the medicalintervention to a user interface.

Example 60: The method of any of examples 56 to 59, wherein determiningthe heart failure status of the patient based on the comparisoncomprises determining, by the processing circuitry, a possibility thatthe patient will experience an adverse medical event.

Example 61: The method of any of examples 56 to 60, wherein the at leastone inactivity criterion comprises a value of least one of a patientactivity level, a patient posture, a time of day, a patient heart rate,or a patient respiration rate.

Example 62: The method of any of examples 56 to 61, wherein determiningthat the activity state of the patient no longer satisfies the at leastone inactivity criterion comprises determining that the patient is in anupright posture based on the at least one first signal, and whereindetermining the current value of the at least one heart beat variabilitymetric while the activity state of the patient no longer satisfies theat least one inactivity criterion comprises determining the currentvalue of the at least one heart beat variability metric while thepatient is in the upright posture.

Example 63: The method of example 62, wherein determining that thepatient is in the upright posture and determining that the activitystate of the patient has increased comprises determining that thepatient has awakened from a sleep state, and wherein the predeterminedperiod of time comprises a predetermined period of time after thepatient has awakened from the sleep state.

Example 64: The method of example 63, wherein the predetermined periodof time is a period of time occurring within about 30 minutes after thepatient has awakened from the sleep state.

Example 65: The method of any of examples 56 to 64, wherein determiningthe current value of the at least one heart beat variability metriccomprises determining a representative current value of the at least oneheart beat variability metric based on a plurality of values of the atleast one heart beat variability metric determined during thepredetermined period of time.

Example 66: The method of any of examples 56 to 65, wherein the at leastone heart beat variability metric comprises two or more heart beatvariability metrics, and wherein determining the current value of the atleast one heart beat variability metric comprises determining a currentvalue of each of the at least two heart beat variability metrics.

Example 67: The method of any of examples 56 to 66, wherein the at leastone heart beat variability metric comprises a corresponding at least oneof a T-wave alternans metric, a PR duration metric, a short-termvariability metric, or a phase-rectified signal averaging metric.

Example 68: The method of any of examples 56 to 67, wherein the at leastone heart beat variability metric comprises at least one heart ratevariability metric.

Example 69: The method of any of examples 56 to 68, wherein the baselinevalue of the at least one heart beat variability metric is apatient-specific baseline value of the at least one heart beatvariability metric, and wherein the at least one second signal is atleast one current second signal, the method further comprising:receiving at least one baseline second signal from the one or moresensors; and determining the patient-specific value of the at least onebaseline heart beat variability metric based on the baseline at leastone second signal.

Example 70: The method of any of examples 56 to 69, further comprisingdetermining an arrhythmia-prone status of the patient based on thecomparison.

Example 71: The method of any of examples 56 to 70, wherein the one ormore sensors comprise a plurality of electrodes, and wherein the atleast one second signal comprises a cardiac electrogram signal receivedfrom at least two of the plurality of electrodes.

Example 72: The method of any of examples 56 to 71, wherein the medicaldevice is an implantable medical device configured for implantationwithin the patient

Example 73: The method of example 72, wherein the implantable medicaldevice comprises a housing configured for subcutaneous implantation, andwherein the one or more sensors are positioned on or within the housing.

Example 74: The method of example 72, wherein the implantable medicaldevice comprises a leadless implantable medical device.

Example 75: A system for determining a heart failure status of a patientusing a medical device, the system comprising: the medical device,wherein the medical device comprises one or more sensors; and processingcircuitry. The processing circuitry is configured to: determine that anactivity state of the patient satisfies at least one inactivitycriterion based on at least one first signal received from the one ormore sensors; determine that the activity state of the patient hasincreased by determining that the activity state of the patient nolonger satisfies the at least one inactivity criterion based on the atleast one first signal after determining that the activity state of thepatient satisfies the at least one inactivity criterion; determine,within a predetermined period of time after determining that theactivity state of the patient has increased, a current value of at leastone heart beat variability metric while the activity state of thepatient no longer satisfies the at least one the inactivity criterionbased on at least one second signal received from the one or moresensors; compare the current value of the at least one heart beatvariability metric to a baseline value of the at least one heart beatvariability metric; and determine the heart failure status of thepatient based on the comparison.

Example 76: The system of example 75, wherein the processing circuitryis configured to compare the current value of the at least one heartbeat variability metric to the baseline value of the at least one heartbeat variability metric by at least determining whether a differencebetween the current value of the at least one heart beat variabilitymetric and the baseline value of the at least one heart beat variabilitymetric satisfies at least one corresponding heart beat variabilitythreshold value that is associated with a change in the heart failurestatus of the patient.

Example 77: The system of example 76, wherein the predetermined periodof time is a current predetermined period of time, and wherein the atleast one corresponding heart beat variability threshold value is basedon one or more corresponding previous values of the at least one heartbeat variability metric that correspond to one or more previouspredetermined periods of time.

Example 78: The system of any of examples 75 to 77, wherein theprocessing circuitry is configured to: transmit the heart failure statusof the patient to a remote computer; receiving, from the remotecomputer, instructions for a medical intervention based on the heartfailure status of the patient; and transmit the instructions for themedical intervention to a user interface.

Example 79: The system of any of examples 75 to 78, wherein theprocessing circuitry is configured to determine the heart failure statusof the patient based on the comparison by at least determining apossibility that the patient will experience an adverse medical event.

Example 80: The system of any of examples 75 to 79, wherein the at leastone inactivity criterion comprises a value of least one of a patientactivity level, a patient posture, a time of day, a patient heart rate,or a patient respiration rate.

Example 81: The system of any of examples 75 to 80, wherein theprocessing circuitry is configured to determine that the activity stateof the patient no longer satisfies the at least one inactivity criterionby at least determining that the patient is in an upright posture basedon the at least one first signal, and wherein the processing circuitryis configured to determine the current value of the at least one heartbeat variability metric while the activity state of the patient nolonger satisfies the at least one inactivity criterion by at leastdetermining the current value of the at least one heart beat variabilitymetric while the patient is in the upright posture.

Example 82: The system of example 81, wherein the processing circuitryis configured to determine that the patient is in the upright postureand determining that the activity state of the patient has increased byat least determining that the patient has awakened from a sleep state,and wherein the predetermined period of time comprises a predeterminedperiod of time after the patient has awakened from the sleep state.

Example 83: The system of example 82, wherein the predetermined periodof time is a period of time occurring within about 30 minutes after thepatient has awakened from the sleep state.

Example 84: The system of any of examples 75 to 83, wherein theprocessing circuitry is configured to determine the current value of theat least one heart beat variability metric by at least determining arepresentative current value of the at least one heart beat variabilitymetric based on a plurality of values of the at least one heart beatvariability metric determined during the predetermined period of time.

Example 85: The system of any of examples 75 to 84, wherein the at leastone heart beat variability metric comprises two or more heart beatvariability metrics, and wherein the processing circuitry is configuredto determine the current value of the at least one heart beatvariability metric by at least determining a current value of each ofthe at least two heart beat variability metrics.

Example 86: The system of any of examples 75 to 85, wherein the at leastone heart beat variability metric comprises a corresponding at least oneof a T-wave alternans metric, a PR duration metric, a short-termvariability metric, or a phase-rectified signal averaging metric.

Example 87: The system of any of examples 75 to 86, wherein the at leastone heart beat variability metric comprises at least one heart ratevariability metric.

Example 88: The system of any of examples 75 to 87, wherein the baselinevalue of the at least one heart beat variability metric is apatient-specific baseline value of the at least one heart beatvariability metric, and wherein the at least one second signal is atleast one current second signal, wherein the processing circuitry isfurther configured to: receive at least one baseline second signal fromthe one or more sensors; and determine the patient-specific value of theat least one baseline heart beat variability metric based on thebaseline at least one second signal.

Example 89: The system of any of examples 75 to 88, wherein theprocessing circuitry is further configured to determine anarrhythmia-prone status of the patient based on the comparison.

Example 90: The system of any of examples 75 to 89, wherein the one ormore sensors comprise a plurality of electrodes, and wherein the atleast one second signal comprises a cardiac electrogram signal receivedfrom at least two of the plurality of electrodes.

Example 91: The system of any of examples 75 to 90, wherein the medicaldevice is an implantable medical device configured for implantationwithin the patient.

Example 92: The system of example 91, wherein the implantable medicaldevice comprises a housing configured for subcutaneous implantation, andwherein the one or more sensors are positioned on the housing.

Example 93: The system of example 91, wherein the implantable medicaldevice comprises a leadless implantable medical device.

Example 94: A non-transitory computer-readable medium storinginstructions for causing processing circuitry to perform a method fordetermining a heart failure status of a patient using a medical devicecomprising one or more sensors, the method comprising, by processingcircuitry of a medical device system comprising the medical device:determining that an activity state of the patient satisfies at least oneinactivity criterion based on at least one first signal received fromthe one or more sensors; determining that the activity state of thepatient has increased by determining that the activity state of thepatient no longer satisfies the at least one inactivity criterion basedon the at least one first signal after determining that the activitystate of the patient satisfies the at least one inactivity criterion;determining, within a predetermined period of time after determiningthat the activity state of the patient has increased, a current value ofat least one heart beat variability metric while the activity state ofthe patient no longer satisfies the at least one the inactivitycriterion based on at least one second signal received from the one ormore sensors; comparing the current value of the at least one heart beatvariability metric to a baseline value of the at least one heart beatvariability metric; and determining the heart failure status of thepatient based on the comparison.

Example 95: The non-transitory computer-readable medium of example 94,wherein comparing the current value of the at least one heart beatvariability metric to the baseline value of the at least one heart beatvariability metric comprises determining whether a difference betweenthe current value of the at least one heart beat variability metric andthe baseline value of the at least one heart beat variability metricsatisfies at least one corresponding heart beat variability thresholdvalue that is associated with a change in the heart failure status ofthe patient.

Various aspects of the disclosure have been described. These and otheraspects are within the scope of the following claims.

What is claimed is:
 1. A system for determining a heart failure statusof a patient using a medical device, the system comprising: the medicaldevice, wherein the medical device comprises one or more sensors; andprocessing circuitry configured to: determine that an activity state ofthe patient satisfies at least one inactivity criterion based on atleast one first signal received from the one or more sensors; determinea first value of at least one heart beat variability metric of thepatient while the activity state of the patient satisfies the at leastone inactivity criterion based on at least one second signal receivedfrom the one or more sensors; determine, after determining the firstvalue of the at least one heart beat variability metric, that theactivity state of the patient no longer satisfies the at least oneinactivity criterion based on the at least one first signal; determine,in response to and within a predetermined period of time afterdetermining that the activity state of the patient no longer satisfiesthe at least one inactivity criterion, a second value of the at leastone heart beat variability metric while the activity state of thepatient no longer satisfies the at least one inactivity criterion basedon the at least one second signal, the predetermined period of timebeing less than thirty minutes; determine a difference between the firstvalue of the at least one heart beat variability metric and the secondvalue of the at least one heart beat variability metric; and generatethe heart failure status of the patient, based on the difference, to beoutput to a remote computer to determine instructions for medicalintervention based on the heart failure status.
 2. The system of claim1, wherein the processing circuitry is configured to generate the heartfailure status of the patient based on the difference between the firstvalue of the at least one heart beat variability metric and the secondvalue of the at least one heart beat variability metric by at leastdetermining whether the difference between the first value of the atleast one heart beat variability metric and the second value of the atleast one heart beat variability metric satisfies a heart beatvariability difference threshold value that is associated with a changein the heart failure status of the patient.
 3. The system of claim 2,wherein the at least one second signal is a current at least one secondsignal, the first value of the at least one heart beat variabilitymetric is a current first value of the at least one heart beatvariability metric, and the second value of the at least one heart beatvariability metric is a current second value of the at least one heartbeat variability metric, wherein the processing circuitry is furtherconfigured to: receive at least one baseline second signal from at leasttwo of a plurality of electrodes that are the same as or different fromthe one or more sensors from which the processing circuitry receives thecurrent at least one second signal; determine a baseline first value ofthe at least one heart beat variability metric and a baseline secondvalue of the at least one heart beat variability metric based on the atleast one baseline second signal; determine a baseline differencebetween the baseline first value of the at least one heart beatvariability metric and the baseline second value of the at least oneheart beat variability metric; and determine the heart beat variabilitydifference threshold value based on the difference between the baselinefirst value of the at least one heart beat variability metric and thebaseline second value of the at least one heart beat variability metric.4. The system of claim 1, wherein the processing circuitry is furtherconfigured to: transmit the heart failure status of the patient to theremote computer; receive, from the remote computer, instructions formedical intervention based on the heart failure status of the patient;and transmit the instructions for medical intervention to a userinterface.
 5. The system of claim 1, wherein the processing circuitry isconfigured to generate the heart failure status of the patient based onthe difference by at least determining a possibility that the patientwill experience an adverse medical event.
 6. The system of claim 1,wherein the at least one inactivity criterion comprises a value of leastone of a patient activity level, a patient posture, a time of day, apatient heart rate, or a patient respiration rate.
 7. The system ofclaim 1, wherein the processing circuitry is configured to determine,prior to determining that the activity state of the patient no longersatisfies the at least one inactivity criterion, that a period of timeduring which the activity state of the patient is expected to satisfythe at least one inactivity criterion has elapsed.
 8. The system ofclaim 1, wherein the processing circuitry is configured to: determinethat the activity state of the patient satisfies the at least oneinactivity criterion by at least determining that the patient is lyingdown based on the at least one first signal; determine the first valueof the at least one heart beat variability metric while the activitystate of the patient satisfies the at least one inactivity criterion byat least determining the first value of the at least one heart beatvariability metric while the patient is lying down; and determine thatthe activity state of the patient no longer satisfies the at least oneinactivity criterion by at least determining that the patient is in anupright posture based on the at least one first signal, and wherein theprocessing circuitry is configured to determine the second value of theat least one heart beat variability metric while the activity level ofthe patient no longer satisfies the at least one inactivity criterion byat least determining the second value of the at least one heart beatvariability metric while the patient is in the upright posture.
 9. Thesystem of claim 1, wherein the first value of the at least one heartbeat variability metric comprises a value of at least one sleeping heartbeat variability metric and the second value of the at least one heartbeat variability metric comprises a value of at least one waking heartbeat variability metric, wherein the processing circuitry is configuredto determine that the activity state of the patient no longer satisfiesthe at least one inactivity criterion after determining the first valueof the at least one heart beat variability metric comprises determiningthat the patient has awakened from a sleep state, and wherein thepredetermined period of time comprises a predetermined period of timeafter the patient has awakened from the sleep state.
 10. The system ofclaim 1, wherein the at least one heart beat variability metriccomprises a corresponding at least one of a T-wave alternans metric, aPR duration metric, a short-term variability metric, or aphase-rectified signal averaging metric.
 11. The system of claim 1,wherein the at least one heart beat variability metric comprises atleast one heart rate variability metric.
 12. The system of claim 1,wherein the medical device is an implantable medical device configuredfor implantation within the patient.
 13. The system of claim 12, whereinthe implantable medical device comprises a housing configured forsubcutaneous implantation, and wherein the one or more sensors arepositioned on or within the housing.
 14. The system of claim 12, whereinthe implantable medical device comprises a leadless implantable medicaldevice.
 15. A system for determining a heart failure status of a patientusing a medical device, the system comprising: the medical device,wherein the medical device comprises one or more sensors; and processingcircuitry configured to: determine that an activity state of the patientsatisfies at least one inactivity criterion based on at least one firstsignal received from the one or more sensors; determine a first value ofat least one heart beat variability metric of the patient while theactivity state of the patient satisfies the at least one inactivitycriterion based on at least one second signal received from the one ormore sensors; determine, after determining the first value of the atleast one heart beat variability metric, that the activity state of thepatient no longer satisfies the at least one inactivity criterion basedon the at least one first signal; determine, in response to and within apredetermined period of time after determining that the activity stateof the patient no longer satisfies the at least one inactivitycriterion, a second value of the at least one heart beat variabilitymetric while the activity state of the patient no longer satisfies theat least one inactivity criterion based on the at least one secondsignal; determine a difference between the first value of the at leastone heart beat variability metric and the second value of the at leastone heart beat variability metric; and generate the heart failure statusof the patient, by at least determining whether the difference betweenthe first value of the at least one heart beat variability metric andthe second value of the at least one heart beat variability metricsatisfies a heart beat variability difference threshold value that isassociated with a change in the heart failure status of the patient, tobe output to a remote computer to determine instructions for medicalintervention based on the heart failure status, wherein the differencebetween the first value of the at least one heart beat variabilitymetric and the second value of the at least one heart beat variabilitymetric corresponds to a current activity cycle of the patient, anactivity cycle comprising a period of time in which the processingcircuitry both determines that the activity state of the patientsatisfies the at least one inactivity criterion and determines that theactivity state of the patient no longer satisfies the at least oneinactivity criterion, wherein the heart beat variability differencethreshold value is based on one or more values of the difference betweenthe first value of the at least one heart beat variability metric andthe second value of the at least one heart beat variability metric thatcorrespond to one or more previous activity cycles of the patient.
 16. Anon-transitory computer-readable medium storing instructions for causingprocessing circuitry to perform a method for determining a heart failurestatus of a patient using a medical device comprising one or moresensors, the method comprising, by processing circuitry of a medicaldevice system comprising the medical device: determining that anactivity state of the patient satisfies at least one inactivitycriterion based on at least one first signal received from the one ormore sensors; determining a first value of at least one heart beatvariability metric of the patient while the activity state of thepatient satisfies the at least one inactivity criterion based on atleast one second signal received from the one or more sensors;determining, after determining the first value of the at least one heartbeat variability metric, that the activity state of the patient nolonger satisfies the at least one inactivity criterion based on the atleast one first signal; determining, in response to and within apredetermined period of time after determining that the activity stateof the patient no longer satisfies the at least one inactivitycriterion, a second value of the at least one heart beat variabilitymetric while the activity state of the patient no longer satisfies theat least one inactivity criterion based on the at least one secondsignal, the predetermined period of time being less than thirty minutes;determining a difference between the first value of the at least oneheart beat variability metric and the second value of the at least oneheart beat variability metric; and generating the heart failure statusof the patient, based on the difference, to be output to a remotecomputer to determine instructions for medical intervention based on theheart failure status.
 17. A system for determining a heart failurestatus of a patient using a medical device, the system comprising: themedical device, wherein the medical device comprises one or moresensors; and processing circuitry configured to: determine that anactivity state of the patient satisfies at least one inactivitycriterion based on at least one first signal received from the one ormore sensors; determine that the activity state of the patient hasincreased by determining that the activity state of the patient nolonger satisfies the at least one inactivity criterion based on the atleast one first signal after determining that the activity state of thepatient satisfies the at least one inactivity criterion; determine, inresponse to and within a predetermined period of time after determiningthat the activity state of the patient has increased, a current value ofat least one heart beat variability metric while the activity state ofthe patient no longer satisfies the at least one the inactivitycriterion based on at least one second signal received from the one ormore sensors, the predetermined period of time being less than thirtyminutes; compare the current value of the at least one heart beatvariability metric to a baseline value of the at least one heart beatvariability metric; and generate the heart failure status of thepatient, based on the comparison, to be output to a remote computer todetermine instructions for medical intervention based on the heartfailure status.
 18. An implantable medical device comprising: one ormore sensors; and processing circuitry configured to: determine that anactivity state of the patient satisfies at least one inactivitycriterion based on at least one first signal received from the one ormore sensors; determine a first value of at least one heart beatvariability metric of the patient while the activity state of thepatient satisfies the at least one inactivity criterion based on atleast one second signal received from the one or more sensors;determine, after determining the first value of the at least one heartbeat variability metric, that the activity state of the patient nolonger satisfies the at least one inactivity criterion based on the atleast one first signal; determine, in response to and within apredetermined period of time after determining that the activity stateof the patient no longer satisfies the at least one inactivitycriterion, a second value of the at least one heart beat variabilitymetric while the activity state of the patient no longer satisfies theat least one inactivity criterion based on the at least one secondsignal, the predetermined period of time being less than thirty minutes;determine a difference between the first value of the at least one heartbeat variability metric and the second value of the at least one heartbeat variability metric; and generate the heart failure status of thepatient, based on the difference, to be output to a remote computer todetermine instructions for medical intervention based on the heartfailure status.
 19. The device of claim 18, wherein the processingcircuitry is configured to generate the heart failure status of thepatient based on the difference between the first value of the at leastone heart beat variability metric and the second value of the at leastone heart beat variability metric by at least determining whether thedifference between the first value of the at least one heart beatvariability metric and the second value of the at least one heart beatvariability metric satisfies a heart beat variability differencethreshold value that is associated with a change in the heart failurestatus of the patient.
 20. The device of claim 19, wherein the at leastone second signal is a current at least one second signal, the firstvalue of the at least one heart beat variability metric is a currentfirst value of the at least one heart beat variability metric, and thesecond value of the at least one heart beat variability metric is acurrent second value of the at least one heart beat variability metric,wherein the processing circuitry is further configured to: receive atleast one baseline second signal from at least two of a plurality ofelectrodes that are the same as or different from the one or moresensors from which the processing circuitry receives the current atleast one second signal; determine a baseline first value of the atleast one heart beat variability metric and a baseline second value ofthe at least one heart beat variability metric based on the at least onebaseline second signal; determine a baseline difference between thebaseline first value of the at least one heart beat variability metricand the baseline second value of the at least one heart beat variabilitymetric; and determine the heart beat variability difference thresholdvalue based on the difference between the baseline first value of the atleast one heart beat variability metric and the baseline second value ofthe at least one heart beat variability metric.
 21. The device of claim18, wherein the processing circuitry is further configured to: transmitthe heart failure status of the patient to the remote computer; receive,from the remote computer, the instructions for medical interventionbased on the heart failure status of the patient; and transmit theinstructions for medical intervention to a user interface.
 22. Thedevice of claim 18, wherein the processing circuitry is configured todetermine the heart failure status of the patient based on thedifference by at least determining a possibility that the patient willexperience an adverse medical event.
 23. The device of claim 18, whereinthe at least one inactivity criterion comprises a value of least one ofa patient activity level, a patient posture, a time of day, a patientheart rate, or a patient respiration rate.
 24. The device of claim 18,wherein the processing circuitry is configured to determine, prior todetermining that the activity state of the patient no longer satisfiesthe at least one inactivity criterion, that a period of time duringwhich the activity state of the patient is expected to satisfy the atleast one inactivity criterion has elapsed.
 25. The device of claim 18,wherein the processing circuitry is configured to: determine that theactivity state of the patient satisfies the at least one inactivitycriterion by at least determining that the patient is lying down basedon the at least one first signal; determine the first value of the atleast one heart beat variability metric while the activity state of thepatient satisfies the at least one inactivity criterion by at leastdetermining the first value of the at least one heart beat variabilitymetric while the patient is lying down; and determine that the activitystate of the patient no longer satisfies the at least one inactivitycriterion by at least determining that the patient is in an uprightposture based on the at least one first signal, and wherein theprocessing circuitry is configured to determine the second value of theat least one heart beat variability metric while the activity level ofthe patient no longer satisfies the at least one inactivity criterion byat least determining the second value of the at least one heart beatvariability metric while the patient is in the upright posture.
 26. Thedevice of claim 18, wherein the first value of the at least one heartbeat variability metric comprises a value of at least one sleeping heartbeat variability metric and the second value of the at least one heartbeat variability metric comprises a value of at least one waking heartbeat variability metric, wherein the processing circuitry is configuredto determine that the activity state of the patient no longer satisfiesthe at least one inactivity criterion after determining the first valueof the at least one heart beat variability metric comprises determiningthat the patient has awakened from a sleep state, and wherein thepredetermined period of time comprises a predetermined period of timeafter the patient has awakened from the sleep state.
 27. The device ofclaim 18, wherein the at least one heart beat variability metriccomprises a corresponding at least one of a T-wave alternans metric, aPR duration metric, a short-term variability metric, or aphase-rectified signal averaging metric.
 28. The device of claim 18,wherein the at least one heart beat variability metric comprises atleast one heart rate variability metric.
 29. The device of claim 18further comprising: a housing configured for subcutaneous implantation,wherein the one or more sensors are positioned on or within the housing.30. The device of claim 18, wherein the implantable medical devicecomprises a leadless implantable medical device.
 31. An implantablemedical device comprising: one or more sensors; and processing circuitryconfigured to: determine that an activity state of the patient satisfiesat least one inactivity criterion based on at least one first signalreceived from the one or more sensors; determine a first value of atleast one heart beat variability metric of the patient while theactivity state of the patient satisfies the at least one inactivitycriterion based on at least one second signal received from the one ormore sensors; determine, after determining the first value of the atleast one heart beat variability metric, that the activity state of thepatient no longer satisfies the at least one inactivity criterion basedon the at least one first signal; determine, in response to and within apredetermined period of time after determining that the activity stateof the patient no longer satisfies the at least one inactivitycriterion, a second value of the at least one heart beat variabilitymetric while the activity state of the patient no longer satisfies theat least one inactivity criterion based on the at least one secondsignal; determine a difference between the first value of the at leastone heart beat variability metric and the second value of the at leastone heart beat variability metric; and generate the heart failure statusof the patient, by at least determining whether the difference betweenthe first value of the at least one heart beat variability metric andthe second value of the at least one heart beat variability metricsatisfies a heart beat variability difference threshold value that isassociated with a change in the heart failure status of the patient, tobe output to a remote computer to determine instructions for medicalintervention based on the heart failure status, wherein the differencebetween the first value of the at least one heart beat variabilitymetric and the second value of the at least one heart beat variabilitymetric corresponds to a current activity cycle of the patient, anactivity cycle comprising a period of time in which the processingcircuitry both determines that the activity state of the patientsatisfies the at least one inactivity criterion and determines that theactivity state of the patient no longer satisfies the at least oneinactivity criterion, wherein the heart beat variability differencethreshold value is based on one or more values of the difference betweenthe first value of the at least one heart beat variability metric andthe second value of the at least one heart beat variability metric thatcorrespond to one or more previous activity cycles of the patient.